data engineer

Figma

<div>
 Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma&rsquo;s platform helps teams bring ideas to life&mdash;whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us!
 
 
 We are looking for an experienced Data Engineer to partner with our Data Science and Data Infrastructure teams to own and scale our data pipelines. You&rsquo;ll also work closely with stakeholders across business teams including sales, marketing, and finance to ensure that the data they need arrives promptly and reliably. You&rsquo;ll play an integral role in building the metrics and self-serve reporting capabilities to unlock Figma&rsquo;s next phase of growth.
 
 
 This is a great role for an individual who is passionate about working with data and data systems, and who loves solving problems. You&rsquo;ll have a good sense for when it makes sense to build fast, scrappy solutions to unblock a key stakeholder vs. when to push back or bring in an outside service. The ideal candidate will be a great communicator who can help coordinate across multiple internal and external teams and takes pride in building end-to-end projects.
 
 
 This is a full time role that can be held from one of our US hubs or remotely in the United States.
 
 
 
 What you'll do at Figma
 
 
 :
 
 
 
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 Own, build, and maintain scalable data pipelines that connect various cloud data sources.
 </li>
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 Develop a deep understanding of Figma&rsquo;s core data models and optimize data pipelines for scale.
 </li>
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 Partner with the Data Science and Data Infrastructure teams to build new foundational data sets that are trusted, well understood, and enable self-service.
 </li>
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 Work with a wide range of cross-functional stakeholders to derive requirements and architect shared datasets; ability to document, simplify and explain complex problems to different types of audiences.
 </li>
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 Establish best practices for the development of specialized data sets for analytics and modeling.
 
 
 </li>
 </ul>
 We'd love to hear from you if you have:
 
 
 <ul>
 <li>
 4+ years in a relevant field.
 </li>
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 Fluency with both SQL and Python.
 </li>
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 Familiarity with Snowflake, dbt, Dagster, and ETL/reverse ETL tools.
 </li>
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 Excellent judgment and creative problem solving skills.
 </li>
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 A self-starting mindset along with strong communication and collaboration skills.
 
 
 </li>
 </ul>
 While not required, it&rsquo;s an added plus if you also have:
 
 
 <ul>
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 Knowledge in data modeling methodologies to design and build robust data architectures for insightful analytics.
 </li>
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 Experience with business systems such as Salesforce, Customer IO, Stripe, NetSuite is a big plus.
 
 
 </li>
 </ul>
 At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you&rsquo;re excited about this role but your past experience doesn&rsquo;t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
 
 
 
 Pay Transparency Disclosure
 
 
 
 If based in Figma&rsquo;s San Francisco or New York hub offices, this role has the annual base salary range stated below.
 
 
 Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. The listed range is a guideline, and the range for this role may be modified. For roles that are available to be filled remotely, the pay range is localized according to employee work location by a factor of between 80% and 100% of range. Please discuss your specific work location with your recruiter for more information.
 
 
 Figma offers equity to employees, as well a competitive package of additional benefits, including health, dental &amp; vision, retirement with company contribution, parental leave &amp; reproductive or family planning support, mental health &amp; wellness benefits, generous PTO, company recharge days, a learning &amp; development stipend, a work from home stipend, and cell phone reimbursement. Figma also offers sales incentive pay for most sales roles and an annual bonus plan for eligible non-sales roles. Figma&rsquo;s compensation and benefits are subject to change and may be modified in the future.
 
 
 Annual Base Salary Range:
 
 
 $140,000&mdash;$348,000 USD
 
 
 At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status
 
 ,
 
 or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
 
 
 We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com. These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities.
 
 
 Examples of accommodations include but are not limited to:
 
 
 <ul>
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 Holding interviews in an accessible location
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 Enabling closed captioning on video conferencing
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 Ensuring all written communication be compatible with screen readers
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 Changing the mode or format of interviews
 
 
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 To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding.
 
 
 By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice.
</div>

Data Engineer

Whatnot

<div>
 
 
 🚀 Join the Future of Commerce with Whatnot!
 
 
 
 
 Whatnot is the largest livestream shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
 
 
 As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
 
 
 We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.
 
 
 
 💻 Role
 
 
 
 At Whatnot data engineers build foundational data systems that power product development, experimentation, machine learning, and operational decision-making across the company. As an Data Engineer on the Notifications Platform, you will play a critical role in owning and evolving one of the highest-volume and highest-impact data domains at Whatnot.
 
 
 Notifications generate hundreds of millions of events per day, and the underlying data systems support everything from ML iteration and experimentation to adhoc engineering debugging. In this role, you will define the technical direction for the Notifications data mart, harden it to production-grade reliability, and set new patterns for domain-owned data engineering.
 
 
 Working in a highly cross-functional role, you will collaborate closely with Product Engineers, Data Scientists, ML Engineers, and the Analytics Platform team. You&rsquo;ll make key architectural decisions around data modeling, reliability, latency, and cost &mdash; and then make them real.
 
 
 
 On Any Given Day, You Will
 
 
 
 <ul>
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 Own data architecture end-to-end. Define how we capture, model, and serve critical business data&mdash;then implement it in production. You&rsquo;ll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency.
 </li>
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 Build mission-critical pipelines. Develop and operate batch data workflows that process high-volume events related to notifications with tight guarantees for latency, completeness, and accuracy.
 </li>
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 Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and production applications. Establish and enforce modeling standards, ownership boundaries, and data contracts across teams.
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 Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
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 Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
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 Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, self-healing, maintainable data assets.
 
 
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 We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.
 
 
 
 
 👋 You
 
 
 
 Curious about who thrives at Whatnot? We&rsquo;ve found that embodying a low ego, growth mindset, and high-impact drive goes a long way here.
 
 
 As our next Data Engineer, you bring deep experience designing reliable, scalable data systems and are excited to take true ownership of a complex product domain.
 
 
 You should have 5+ years of experience in the data or software engineering domain, plus:
 
 
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 Strong experience building and maintaining production-grade data pipelines with clear SLAs, monitoring, and alerting
 </li>
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 Deep expertise in SQL, including complex model graphs, dependency management, and performance optimization
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 Are comfortable writing production-grade code in Python or SQL languages, and integrating with CI/CD and infrastructure-as-code workflows.
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 Have deep hands-on expertise with modern data tooling across ingestion (e.g., Kafka, Debezium), transformation (dbt, Spark, Flink), orchestration (Dagster, Airflow), and observability (Monte Carlo, Great Expectations).
 </li>
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 Have operated cloud data warehouses such as Snowflake, BigQuery, or Redshift, including schema design, cost optimization, and workload tuning.
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 Proven track record working with large-scale datasets (hundreds of millions of rows per day)
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 Experience designing data models that balance analytics, ML, and operational debugging use cases
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 Strong systems thinking &mdash; you consider correctness, latency, cost, and maintainability together
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 Self-starter ethic, thriving under a high level of autonomy
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 Exceptional interpersonal and communication skills in cross-functional environments
 
 
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 </ul>
 
 💰Compensation For Full-Time (Salary)
 
 
 
 
 US based applicants:
 
 $195,000/year to $230,000/year + benefits + equity.
 
 
 The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills, and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity.
 
 
 
 🎁 Benefits
 
 
 
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 Generous Holiday and Time off Policy
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 Health Insurance options including Medical, Dental, Vision
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 Work From Home Support
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 Home office setup allowance
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 Monthly allowance for cell phone and internet
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 Care benefits
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 Monthly allowance for wellness
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 Annual allowance towards Childcare
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 Lifetime benefit for family planning, such as adoption or fertility expenses
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 Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
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 Monthly allowance to dogfood the app
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 All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!).
 </li>
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 Parental Leave
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 16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
 </li>
 </ul>
 </ul>
 
 
 💛 EOE
 
 
 
 
 Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.
 
 
 Compensation Range: $180K - $260K
 
 
</div>

Data Engineer, Notifications

Notion

<div>
 Who We Are
 
 
 Notion is the collaborative AI workspace where teams and agents think together. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.
 
 
 Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn&rsquo;t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business&rsquo;s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life&rsquo;s work.
 
 
 
 About The Role
 
 
 
 As Notion scales, there is a unique opportunity to build the data foundations for People Analytics that enable better decisions around hiring, performance, compensation, and org design - while maintaining strong privacy guarantees for sensitive employee data.
 
 
 In this role, you will design, build, and operate scalable data systems and pipelines that power People data. Your work will ensure that employee data is reliable, secure, and production-ready, enabling both analytics and AI-driven people workflows across the company.
 
 
 This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We&rsquo;re looking for someone who&rsquo;s excited to work alongside the team during those days.
 
 
 What You'll Achieve
 
 
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 Partner closely with the Chief People Officer and People leadership team to translate strategic questions into clear metrics and data products that inform workforce planning, organization design, performance reviews, compensation discussions, and executive reporting
 </li>
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 Define and build ETL/ELT pipelines and infrastructure to ingest and standardize data from HRIS and adjacent systems such as Workday, ATS, payroll, benefits, performance platforms, creating a reliable source of truth for headcount, org structure, hiring, attrition, and talent health
 </li>
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 Design privacy-first data architecture, including access controls, PII handling, and governance standards
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 Implement data quality, testing, and observability systems to ensure sensitive employee data is accurate and trustworthy
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 Design privacy-aware data models, transformations, semantic layers, and quality checks so sensitive employee data is accurate, timely, well-documented, and governed appropriately with role-based access controls for decision-making and makes AI-powered people tools and automations reliable enough to ship to production
 </li>
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 Partner with People Analytics to translate ambiguous requirements into scalable datasets, dashboards, and self-serve data products that give People leaders, People Partners, and managers visibility into workforce trends while reducing manual overhead.
 
 
 </li>
 </ul>
 Skills You'll Need to Bring
 
 
 <ul>
 <li>
 6+ years of experience in analytics engineering, data engineering, people analytics, or a closely related role, with a track record of building trusted datasets and systems for business decision-making
 </li>
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 Hands-on experience working with HRIS and adjacent People systems data (for example Workday, ATS (Ashby), payroll, benefits, performance, or engagement platforms) and know how to reconcile definitions across messy, evolving source systems
 </li>
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 Proficiency in SQL and data modeling, and can build production-grade transformations, define durable metric logic, and write performant queries against large analytical datasets
 </li>
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 Proficiency in Python (or similar OOP languages) for building data pipelines, automation, and infrastructure
 </li>
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 Experience designing systems with data quality, testing, monitoring, and SLAs in mind
 </li>
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 Experience with modern data tooling and cloud warehouses such as Snowflake, dbt, Airflow, Fivetran, or similar technologies, along with strong instincts around testing, monitoring, and data quality
 </li>
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 Excellent judgment with sensitive and confidential data, and can implement infrastructure and tooling for data privacy, access controls, governance, and thoughtful interpretation of people metrics
 </li>
 <li>
 Effectiveness in partnering directly with the CPO, People Partners, Finance, and business leaders to turn ambiguous questions into scoped solutions and clearly communicated insights
 </li>
 <li>
 Ability to leverage AI and agent-based tools to accelerate development, while defining guardrails for safe, production-grade data workflows
 
 
 </li>
 </ul>
 Nice to Haves
 
 
 <ul>
 <li>
 You have worked at a fast-growing start-up, a SaaS company or are eager to contribute in such an environment (being a current Notion user would be great!)
 
 
 </li>
 </ul>
 Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role&rsquo;s scope and complexity, and the candidate&rsquo;s experience and expertise, and may vary from the range provided below. For roles based in San Francisco, the estimated base salary range for this role is $213,000 to $250,000
 
 
 By clicking &ldquo;Submit Application&rdquo;, I understand and agree that Notion and its affiliates and subsidiaries will collect and process my information in accordance with Notion&rsquo;s Global Recruiting Privacy Policy.
 
 
 A Note on AI
 
 
 You don&rsquo;t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement &mdash; when that&rsquo;s the case, we'll say so explicitly in the qualifications. People who thrive here don&rsquo;t treat AI as a novelty. They use it to think better, and make their work easier for others to build on.
 
 
 Equal Opportunity &amp; Accommodations
 
 
 We hire talented people from a wide range of backgrounds. If you&rsquo;re excited about this role but don&rsquo;t meet every bullet, we still encourage you to apply. Notion is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Consistent with applicable law, we will consider for employment qualified applicants with arrest and conviction records. Notion provides reasonable accommodations during the application process; if you need one, please let your recruiter know.
 
 
 Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.
 
 
</div>

Data Engineer, People Analytics

Salesforce

<div>To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn&rsquo;t a buzzword &mdash; it&rsquo;s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You&rsquo;re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
The Experience
 
Salesforce is building the next-generation Enterprise Knowledge Graph platform to power AI-driven experiences, agentic applications, semantic search, enterprise data discovery, and intelligent decision-making across the company. The platform serves as the foundational knowledge layer connecting enterprise data, business entities, ontologies, and relationships across multiple domains.
 
 
We are seeking both a Senior Member of Technical Staff (SMTS) and a Lead Member of Technical Staff (LMTS) to join our Enterprise Knowledge Graph and AI Engineering team.
 
 
The SMTS will serve as a senior engineer and core systems developer &mdash; heavily hands-on, developing, optimizing, and scaling core knowledge graph components, semantic pipeline workflows, and AI-powered frameworks. You will partner with Lead and Principal Engineers to implement technical designs and build production-ready scalable systems that support agentic AI use cases across the enterprise.
 
 
The LMTS will serve as a hands-on technical lead, systems designer, and ontology engineer &mdash; designing, building, and scaling core knowledge graph infrastructure, semantic schemas, and AI-powered developer frameworks. You will partner closely with Principal Engineers, Product Management, Ontology experts, and Data Engineering teams to turn high-level engineering visions into production-ready scalable foundations.
 
 
Both roles will actively implement and drive AI-powered engineering tools and developer platforms that improve engineering productivity, software quality, and delivery velocity across the organization. 
 
What You'll Actually Be Doing
<ul><li>Design &amp; Implement: Build and scale Salesforce's Enterprise Knowledge Graph platform components, focusing on performance, data throughput, system reliability, high availability, and robust data integrity. (LMTS: Lead hands-on design and implementation of platform subsystems; SMTS: Write high-quality, production-grade code.)
</li><li>Graph &amp; Ontology Engineering: Develop graph data models, write complex graph queries, and construct scalable data pipelines to ingest and map structured and unstructured data to enterprise ontologies and taxonomies. (LMTS: Also design enterprise ontologies, taxonomies, semantic layers, entity resolution frameworks, graph APIs, and vector search capabilities to support advanced RAG and agentic workflows.)
</li><li>Semantic Routing: Write and maintain Python-based semantic routing frameworks to parse, classify, and dynamically direct incoming queries to the appropriate knowledge graph indexes or vector databases. (LMTS: Design, optimize, and productionize routing frameworks at enterprise scale, steering queries to appropriate knowledge graphs, ontology sub-graphs, or vector databases.)
</li><li>AI Tooling &amp; Automation: Build, integrate, and leverage AI-powered developer tools and engineering automation platforms utilizing ecosystems such as Claude, Cursor, Windsurf, AI Agents, and Model Context Protocol (MCP) frameworks. (LMTS: Also develop, deploy, and optimize these tools; drive strategy and productionization.)
</li><li>Data Integration: Build scalable data pipelines and engineering patterns to ingest, transform, and orchestrate structured, unstructured, and third-party data sources into graph-based platforms mapped tightly to enterprise ontologies.
</li><li>Feature Ownership &amp; Technical Execution: Own the technical execution of specific platform features from concept through design, coding, testing, and production deployment. (LMTS: Also translate high-level technical visions and roadmaps into concrete system blueprints, ontology schemas, and execution plans.)
</li><li>Code Quality &amp; Rigor: Participate heavily in code reviews, write comprehensive automated unit/integration tests, and ensure adherence to engineering standards and operational best practices.
</li><li>Technical Mentorship: Provide technical guidance and mentorship to engineers on the team. (SMTS: Mentor MTS and Associate engineers. LMTS: Provide day-to-day guidance, code reviews, and design direction to SMTS, MTS, and associate engineers, fostering a culture of technical rigor and operational maturity.)
</li><li>Cross-Functional Collaboration: Work closely with Lead/Principal Engineers, Product Managers, and Data Engineering teams to deliver robust features aligned with broader enterprise AI priorities. (LMTS: Also partner with PMTS engineers and Ontology governance boards to ensure alignment with AI infrastructure standards.)
</li><li>Evaluate &amp; Innovate (LMTS): Conduct deep-dive evaluations of emerging graph technologies, ontology modeling tools, semantic reasoning frameworks, vector databases, and AI tooling to continuously modernize the platform.
</li></ul>You're Our Person If...
SMTS
<ul><li>Experience: 8+ years of hands-on software engineering experience in development, data engineering, distributed systems, or enterprise data platforms.
</li><li>Education: A related technical degree required.
</li><li>Core Programming: Expert-level coding skills in backend ecosystems, with strong fluency in Python and standard object-oriented/functional programming languages.
</li><li>Semantic Routing &amp; AI: Hands-on experience developing and deploying custom semantic routers using Python (leveraging native embeddings, LangChain, or mathematical logic like cosine similarity) alongside RAG architectures, vector search platforms, and AI workflows.
</li><li>Graph &amp; Ontology Fundamentals: Solid experience working with graph databases and semantic web concepts (e.g., Neo4j, RDF/OWL, SPARQL, property graphs) and mapping data to structured taxonomies.
</li><li>Developer Tooling: Practical experience configuring, testing, or integrating AI-assisted engineering tools or automation workflows (e.g., Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks).
</li><li>Distributed Systems &amp; Cloud: Proven experience building applications on cloud-native systems (AWS, GCP, or Azure) utilizing microservices, REST/gRPC APIs, and event-driven data streaming (e.g., Kafka).
</li><li>Delivery: Track record of owning and successfully delivering complex features in an agile, production-scale environment.
</li></ul>LMTS
<ul><li>Experience: 10+ years of hands-on experience in software engineering, data engineering, distributed systems, or enterprise data platforms.
</li><li>Education: A related technical degree required.
</li><li>Ontology &amp; Graph Expertise: Solid, hands-on experience designing and building Knowledge Graph platforms, formal ontologies, semantic models, taxonomies, or enterprise metadata management systems.
</li><li>Tooling &amp; Ecosystems: Strong hands-on experience with graph technologies and ontology engineering tools (e.g., Neo4j, TopQuadrant, Prot&eacute;g&eacute;, RDF/OWL, SPARQL, SHACL, property graphs) and semantic reasoning frameworks.
</li><li>AI &amp; Retrieval: Proven experience implementing graph-powered AI solutions, vector search platforms, Retrieval-Augmented Generation (RAG) architectures, and orchestrating agentic workflows.
</li><li>Semantic Routing Mastery: Demonstrated hands-on experience designing, optimizing, and productionizing custom semantic routers using Python (leveraging native embeddings, LangChain, semantic-router, or specialized mathematical logic like cosine similarity) to decouple intent handling from expensive LLM calls.
</li><li>Developer Automation: Experience deploying and integrating AI-assisted engineering tools or automation workflows using ecosystems like Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks.
</li><li>Backend &amp; Cloud: Strong experience with cloud-native system designs (AWS, GCP, or Azure), distributed systems, microservices, and high-throughput event-driven systems.
</li><li>Leadership: Demonstrated experience leading feature teams, guiding technical execution, and mentoring mid-to-senior level engineers.
</li></ul>Even Better If...
SMTS
<ul><li>Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.
</li><li>Familiarity with ontology validation frameworks (e.g., SHACL) and data quality governance.
</li><li>Experience building integrations with data platform environments like Salesforce Data Cloud or enterprise CRM metadata architectures.
</li><li>Experience optimizing low-latency applications and heavy-throughput vector search lookups.
</li><li>Passion for engineering automation and driving personal/team velocity via advanced AI development tools.
</li></ul>LMTS
<ul><li>Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field with a focus on Semantic Web or Knowledge Representation.
</li><li>Direct experience integrating platforms with Salesforce Data Cloud, CRM platforms, or metadata-driven system designs.
</li><li>Experience with semantic routing at enterprise scale, high-throughput enterprise search systems, and graph-powered recommendation engines.
</li><li>Deep familiarity with advanced ontology governance, federated knowledge management, and data contract alignment.
</li><li>Proven track record of optimizing engineering team velocity through the tailored implementation of AI developer tooling.
</li></ul>Unleash Your Potential
When you join Salesforce, you&rsquo;ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we&rsquo;ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future &mdash; but to redefine what&rsquo;s possible &mdash; for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates&rsquo; resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that&rsquo;s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications &ndash; without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.&amp;#xa;&amp;#xa;The typical base salary range for this position is $148,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $178,900 - $285,800 annually.&amp;#xa;&amp;#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.</div>

Data Engineer (SMTS/LMTS) - Knowledge Graph & AI

MetaVoice

<div>
 
 At MetaVoice, our goal is an
 
 AI for voice conversations that's as natural as talking to a person.
 
 
 
 
 
 
 Today's voice AI fails at real-world conversations. It&rsquo;s slow, turn-based like a walkie-talkie, breaks with interruptions, and doesn&rsquo;t understand emotion.
 
 
 
 
 
 Developers can't build compelling experiences and users disengage. This limits voice AI to simple receptionist tasks and basic customer support, blocking meaningful services (sales, therapy, coaching) where dialogue and emotional intelligence matter most. Scaling current tech does not work.
 
 
 
 
 
 We're building voice AI that's as natural as talking to a person. Our approach is a duplex speech-to-speech model that learns conversational behaviour directly from data.
 
 
 
 
 
 MetaVoice is founded by:
 
 <ul>
 <li>
 Sid, founding engineer at Wayve.ai ( $2.5B+ raised)
 </li>
 <li>
 Vatsal, co-created Alexa&rsquo;s first AI voice.
 </li>
 </ul>
 
 
 
 
 We&rsquo;re passionate about building products people truly love.
 
 
 
 
 
 
 The role
 
 
 
 This is a founding engineer role owning the ML data &amp; evaluation platform that model development runs on.
 
 
 
 
 
 
 Who we're looking for
 
 
 
 You&rsquo;ve worked on distributed ML infra and understand that model quality is often a data problem. You:
 
 <ul>
 <li>
 Have a track record of taking initiative and learning things quickly.
 </li>
 <li>
 Care deeply about the people using the systems you build, whether they're researchers, operators or customers
 </li>
 <li>
 Have built something yourself (startup, side project, etc) or worked in an early-stage startup
 </li>
 </ul>
 
 
 
 
 
 What you've done
 
 
 <ul>
 <li>
 Built distributed ML data pipelines to generate datasets for training and evaluation, including curation, sampling and slicing.
 </li>
 <li>
 Built and operated MLOps infrastructure to train, evaluate and serve ML models, including versioning, orchestration and observability.
 </li>
 <li>
 Built eval systems to measure model quality and catch regressions.
 </li>
 <li>
 Developed internal tools and workflows for researchers and operators to explore data and iterate fast.
 </li>
 </ul>
 
 Strong plus:
 
 <ul>
 <li>
 Designed data flywheels: automated data collection and model update loops.
 </li>
 <li>
 Designed evaluation frameworks: automated or human-in-the-loop
 </li>
 <li>
 Experience with speech, audio, or conversational datasets
 </li>
 </ul>
 
 
 
 
 
 What we offer
 
 
 <ul>
 <li>
 Change the world (when we succeed)
 </li>
 <li>
 Environment to do the best work of your life.
 </li>
 <li>
 Small, humble and talent dense team.
 </li>
 <li>
 Opportunity to make cutting-edge research work in real-world scenarios.
 </li>
 <li>
 Work with 100+ TBs of data and 10B+ parameter models.
 </li>
 </ul>
 
 
 
 
 
 Culture
 
 
 <ul>
 <li>
 We're an in-person team in San Francisco &amp; love working together. It helps us learn from one another and make decisions quickly.
 </li>
 <li>
 We ship fast &amp; obsess over making customers happy.
 </li>
 <li>
 We offer high autonomy, allowing everyone to do their best work.
 </li>
 </ul>
 
 
 
 
 
 Compensation &amp; Benefits
 
 
 <ul>
 <li>
 Significant equity as a founding team member.
 </li>
 <li>
 $180K-300K base.
 </li>
 <li>
 Immigration support, we are immigrants ourselves.
 </li>
 <li>
 Fully covered medical, dental, and vision insurance
 </li>
 </ul>
 
 
 
 
 
 Interview Process
 
 
 
 We move with speed, and aim to go from first interview to offer within a week.
 
 <ul>
 <li>
 
 Vibe check, 30 mins:
 
 chat with one of the founders about the mission, product and your background
 </li>
 <li>
 
 Technical Interview: 30 mins
 
 </li>
 <li>
 
 Culture interview: 30 mins
 
 </li>
 <li>
 
 Onsite co-work:
 
 Spend half a day with us. We'll build and iterate on a problem together.
 </li>
 </ul>
 
 
</div>

ML Platform Engineer (Data & Evals)

Hayden AI

<div>
 
 About Us
 
 
 
 At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.
 
 
 From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.
 
 
 
 About The Deep Learning Team
 
 
 
 The Deep learning team&rsquo;s work is at the crux of Hayden AI&rsquo;s solutions to its customers. The team is responsible for building and maintaining advanced models that are able to perceive the world and the knowledge produced by the models is used downstream to implement various customer use-cases. The team runs a number of models both in the cloud as well as on the edge device serving different scenarios. The team also works closely with the platform team to build out Hayden AI&rsquo;s MLOps infrastructure to achieve better scale and reliability.
 
 
 
 About The Role
 
 
 
 As a Deep Learning Engineer at Hayden, you will make key contributions towards building foundational AI capabilities for Hayden that will enable solving complex problems for Hayden AI Customers. In this role, you will build, train, evaluate and deploy state of the art ML models in production at scale. You will report into the Director of AI within the Perception Org and work closely with other engineering as well as product teams to deliver value to Hayden AI customers. We are looking for someone who has familiarity with or growing expertise in at least one of the verticals below &mdash; depth in a specific area is valued but not required on day one.
 
 
 <ul>
 <li>
 3D vision models to predict depth and 3D structure,
 </li>
 <li>
 Video/temporal behavior models to predict intent,
 </li>
 <li>
 Deep understanding of Vision language models and hands-on experience fine tuning them,
 </li>
 <li>
 Deep understanding of foundation models in perception.
 </li>
 <li>
 Deep knowledge of Nvidia edge device stack for running ML models and cuda know-how in terms delivering highly optimized models
 
 
 </li>
 </ul>
 This position is based in San Francisco and follows a hybrid schedule with at least 3 days in-office per week.
 
 
 
 
 Key Responsibilities
 
 
 
 
 Below are your primary responsibilities. These represent the core areas where you&rsquo;ll make an impact. As part of a rapidly evolving team, we look forward to your impact expanding over time.
 
 
 <ul>
 <li>
 Come up with solutions to complex problems in perception using deep learning.
 </li>
 <li>
 Articulate ideas both verbally and in written form culminating in clear design and project plan docs for their work
 </li>
 <li>
 Contribute to team roadmap and planning
 </li>
 <li>
 Work with cross functional teams across the company and contribute towards delivery of end to end solutions for the customer
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 The qualifications below outline the experience and skills most relevant to success in this role. We recognize that skills and potential come in many forms, and we welcome diverse experiences that advance our mission.
 
 
 <ul>
 <li>
 Experience: 1-2 years experience building and deploying machine learning models for perception in production settings.
 </li>
 <li>
 Core Skills: Solid hands-on experience designing, training, and evaluating machine learning models for perception. Demonstrated ability to independently build ML pipelines and deploy models to cloud environments (AWS, GCP, or Azure); familiarity with MLOps practices including experiment tracking, model versioning, and automated workflows. Hands-on experience in PyTorch, Python, and related skills.
 </li>
 <li>
 Personal Attributes:Good communicator, self starter and ability to collaborate with others, quick learner who can adapt to a fast paced startup culture
 </li>
 <li>
 Education: Bachelors or Masters in Computer Science or related field
 </li>
 <li>
 Nice to Have: Experience working in perception problems in self driving car companies that aligns closely with the work of this team.
 
 
 </li>
 </ul>
 Compensation Range: $161,637 - $175,000
 
 
</div>

Deep Learning Engineer

Anthropic

<div><div><h2 class="jobSectionHeader">About Anthropic</h2>

Anthropic&rsquo;s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</div><div><h2 class="jobSectionHeader">
About the role</h2>

The Research Data Platform team builds the tools that Anthropic's researchers use every day to manage, query, and analyze the data that goes into training and evaluating frontier models. We power the internal applications researchers rely on to monitor RL runs, explore finetuning datasets, and understand what's happening inside their experiments.

We're looking for engineers who love working directly with users and who excel at building data products &mdash; the pipelines that move data out of training runs into queryable storage, and the APIs, libraries, and services researchers use to manage and explore it. This role sits closer to the research workflow than a typical data infrastructure position: you'll often embed with research teams, build ML-specific tooling alongside them, and leverage what our Data Infrastructure team has already built rather than reinventing it.

We do not require prior ML or AI training experience. If you enjoy working closely with technical users, learning new domains quickly, and building tools people actually want to use, you'll pick up the research context fast.
<h2 class="jobSectionHeader">
Responsibilities</h2>
<ul><li>Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query</li>
<li>Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis</li>
<li>Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work</li>
<li>Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly</li>
<li>Collaborate with adjacent teams to build on existing systems rather than reinventing them</li>
</ul><h2 class="jobSectionHeader">You may be a good fit if you</h2>
<ul><li>Have significant software engineering experience, particularly building data-intensive applications or internal tooling</li>
<li>Enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted</li>
<li>Are results-oriented, with a bias towards flexibility and impact</li>
<li>Pick up slack, even if it goes outside your job description</li>
<li>Want to learn more about machine learning research</li>
<li>Care about the societal impacts of your work</li>
</ul><h2 class="jobSectionHeader">Strong candidates may also have experience with</h2>
<ul><li>Large-scale ETL, columnar storage formats, and query engines (e.g., Spark, BigQuery, DuckDB, Parquet)</li>
<li>High-volume time series data &mdash; ingestion, storage, and efficient querying</li>
<li>Data cataloging, lineage, or metadata management systems</li>
<li>ML experiment tracking or metrics platforms</li>
<li>Working in environments where engineers partner closely with quantitative users &mdash; research labs, trading firms, observability or analytics startups</li>
<li>Complex data visualization and full-stack web application development</li>
</ul></div><div><div><div><div>
The annual compensation range for this role is listed below.

For sales roles, the range provided is the role&rsquo;s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</div></div>
Annual Salary:
$320,000 - $405,000 USD</div></div><div><h2 class="jobSectionHeader">
Logistics</h2>

Minimum education: Bachelor&rsquo;s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. 
 
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links&mdash;visit anthropic.com/careers directly for confirmed position openings.
<h2 class="jobSectionHeader">
How we're different</h2>

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact &mdash; advancing our long-term goals of steerable, trustworthy AI &mdash; rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
<h2 class="jobSectionHeader">
Come work with us!</h2>

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.</div></div>

Software Engineer, Research Data Platform

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