clinical research associate ii - oncology (remote us - multiple openings)

20-seater bus driver – airline crew transport (1099 contractor) san francisco

32 hour personal banker | sloat & springfield

clinical research associate ii - oncology (remote us - multiple openings) Jobs in San Francisco

clinical research intern Jobs in San Francisco

clinical research intern

advisor - scientific data engineer Jobs in San Francisco

advisor - scientific data engineer

ai content specialist Jobs in San Francisco

ai content specialist

ai engineering lead Jobs in San Francisco

ai engineering lead

ai researcher

Achira

<div><h1 class="jobSectionHeader">Why Achira</h1>
At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.
At Achira, you&rsquo;ll operate at the frontier scale of massive compute, massive data, and massive ambition. You&rsquo;ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We&rsquo;re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.<h1 class="jobSectionHeader">
About the Role</h1>
We&rsquo;re looking for machine learning researchers who want to shape the frontier of generative models for the atomistic microcosm. You will work at the intersection of cutting-edge machine learning, statistical mechanics, and approximate bayesian inference to help us conquer sampling problems at light-speed. In addition, you&rsquo;ll collaborate with experts in chemistry and physics to invent and implement models to unlock what&rsquo;s possible for Achira&rsquo;s microscopic world models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.<h1 class="jobSectionHeader">
What You&rsquo;ll Do</h1><ul><li>
Invent advanced sampling and simulation methods that integrate probabilistic inference, deep learning, and reinforcement learning to enable efficient exploration and simulation of learned energy landscapes for molecular systems.</li><li>
Design and train frontier generative models: diffusion, autoregressive, flow-based, and latent-variable architectures.</li><li>
Build models that can map between data distributions to bridge the gap between simulation and reality.</li><li>
Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira&rsquo;s ecosystem.</li><li>
Collaborate with physicists and chemists to ensure models are grounded in real physics.</li><li>
Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.</li></ul><h1 class="jobSectionHeader">
About You</h1><ul><li>
Interested in building generative models that describe real matter.</li><li>
Drive to build at the frontier of what&rsquo;s possible and try out new, high-risk ideas.</li><li>
Machine learning researcher with professional experience (post-degree) in an industry setting.</li><li>
Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models.</li><li>
Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds.</li><li>
Proficiency in Python and modern ML frameworks (PyTorch, JAX).</li><li>
Experience collaborating on research projects across multi-person teams.</li></ul><h1 class="jobSectionHeader">
Nice to Have</h1>
Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths. If the work excites you, we encourage you to apply, even if you hit none of the bonus features listed below!<ul><li>
Experience working with models that operate on 3-D point clouds and dynamic data.</li><li>
Experience in sequential monte carlo methods.</li><li>
Experience with probabilistic programming.</li><li>
Experience with pre-training, mid-training, and post-training (especially reenforcement learning) parts of the model development process.</li><li>
Familiarity with statistical mechanics: working knowledge of sampling, estimators, and the Crooks/Jarzynski perspective of nonequilibrium statistical mechanics.</li><li>
Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science.</li><li>
Experience working with multi-cloud distributed compute systems.</li><li>
Experience working with multi-site distributed company team.</li></ul>
Compensation Range: $164,638 - $259,000</div>

ML Research Scientist (MLRS) - Generative AI

Plaid

<div><div><h2 class="jobSectionHeader">Location</h2>
San Francisco HQ; New York City Office; Seattle Office
</div><div><h2 class="jobSectionHeader">Address</h2>
1098 Harrison Street, San Francisco, California, 94103
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Location Type</h2>
Hybrid
</div><div><h2 class="jobSectionHeader">Department</h2>
All DepartmentsData
</div><div><h2 class="jobSectionHeader">Compensation</h2>
<ul><li>$211,680 &ndash; $272,160</li>
</ul><div>Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.</div></div></div>
 <div><div>We believe that the way people interact with their finances will drastically improve in the next few years. We&rsquo;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid&rsquo;s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
The Data Foundation &amp; AI team sits within Plaid&rsquo;s Data organization and is responsible for building the shared machine learning and AI infrastructure that powers innovation across Plaid&rsquo;s product portfolio. The team transforms Plaid&rsquo;s unique financial network data into scalable, general-purpose representations that can be leveraged by teams throughout the company. Working across the full model lifecycle, the team develops and curates pretraining datasets, trains and evaluates foundation models, and operates production-grade serving and monitoring systems to ensure reliable, high-impact AI capabilities at scale.
As a Research Scientist, you will advance Plaid&rsquo;s foundation models by developing novel model architectures, pretraining objectives, and fine-tuning strategies that generalize across a broad range of financial and product applications. You will contribute to the design, development, and operation of production machine learning systems, working across the full stack from data and feature engineering to training pipelines, model serving, and monitoring. The role also involves creating comprehensive evaluation frameworks that assess model performance across diverse tasks and use cases, ensuring robust and reliable outcomes. In close partnership with product and engineering teams, you will adapt foundation models to solve specific business challenges, validate their impact through rigorous experimentation, and translate research advances into production capabilities. You will also share insights and results with both internal and external audiences, helping drive innovation and elevate the practice of machine learning and AI across Plaid.
Responsibilities:
<ul><li>Building a foundation model on one of the world&rsquo;s richest financial datasets that no one else has.
</li><li>Doing research that ships: moving from experimentation and prototypes to production systems serving real customers.
</li><li>Working across the full ML stack, from pretraining objectives and architectures to serving infrastructure and monitoring.
</li><li>Collaborating with a high-caliber team and seeing your work amplify the capabilities of multiple product teams.
</li><li>Helping hundreds of millions of consumers achieve greater financial freedom through data-driven products.
</li></ul>Qualifications:
<ul><li>MS or PhD in ML/AI/CS/Stats/Applied Math (or closely related). PhD preferred but not required &mdash; candidates with equivalent industry research and production experience will be considered.
</li></ul><ul><li>1-3 years of industry experience building and deploying ML models, with evidence of both research depth and production delivery
</li><li>Strong applied ML research skills with production delivery experience
</li><li>Depth in Transformers/LLMs, representation learning, or large-scale model training
</li><li>Distributed training experience and strong Python + software engineering fundamentals
</li><li>Fintech / financial data domain experience - Nice to have
</li><li>Demonstrated ability to ship models to production (not just prototype) - Nice to have
</li><li>External publications or open-source contributions - Nice to have
</li></ul>Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com. 

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
Compensation Range: $211,680 - $272,160</div></div>

Machine Learning Engineer (Research Scientist) - DFAI

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 Domain Scaling team has the goal to make Claude world-class at real-world knowledge work in domains like finance, healthcare, and legal. This is a unique role that combines executing directly on applied research and data sourcing (real-world and synthetic) to improve our models. You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.
<h2 class="jobSectionHeader">
Responsibilities</h2>
<ul>
<li>Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training
</li>
<li>Manage technical relationships with external data vendors, including evaluation of data quality and reward design
</li>
<li>Collaborate with domain experts to design data pipelines and evaluations
</li>
<li>Explore novel ways of creating RL envs for high value tasks
</li>
<li>Develop and improve QA frameworks to catch reward hacking and ensure env quality
</li>
<li>Run generalization experiments to measure how data strategy changes improve model capabilities
</li>
<li>Partner with other RL research teams and product teams to translate capability goals into training envs and evals
</li>
</ul><h2 class="jobSectionHeader">You may be a good fit if you</h2>
<ul>
<li>Have experience with fine-tuning large language models for specific domains or real-world use cases
</li>
<li>Have experience with reinforcement learning, reward design, or training data curation for LLMs
</li>
<li>Are comfortable managing technical vendor relationships and iterating quickly on feedback
</li>
<li>Find value in reading through datasets to understand them and spot issues
</li>
<li>Have strong cross-functional collaboration skills
</li>
<li>Are passionate about making AI more useful and accessible across different industries
</li>
<li>Are excited about a role that includes a combination of applied research and hands-on data work
</li>
</ul><h2 class="jobSectionHeader">Strong candidates may also</h2>
<ul>
<li>Have experience training production ML systems
</li>
<li>Have experience designing evals or benchmarks for LLMs
</li>
<li>Have domain expertise in a vertical where we would like to make our models more useful
</li>
<li>Have experience working with external vendors or technical partners
</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:
$350,000 - $850,000 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>

Research Engineer, Domain Scaling

Distyl AI

<div><h1 class="jobSectionHeader">About Distyl AI
</h1>Distyl is an applied AI technology company partnering with the world&rsquo;s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations. 
 
We research and deploy technologies that power AI-native operations &mdash; both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations &mdash; from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys. 
 
Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.<h1 class="jobSectionHeader">
What We Are Looking For</h1>
Forward Deployed AI Engineers build and operate production AI systems that deliver business value inside customer environments. This role is for engineers who thrive in ambiguous problem spaces, take ownership of outcomes, and want to work directly on AI systems that must perform reliably under enterprise constraints.
Forward Deployed AI Engineers are hands-on builders. They design, implement, deploy, and iterate on end-to-end AI systems in close partnership with customers, subject matter experts, and other Distyl engineers. They translate messy operational needs into concrete system behavior, build the software and AI workflows required to support that behavior, and continuously improve systems through evaluation, feedback, integration, and production iteration.
This is not a demo-building role. Forward Deployed AI Engineers are expected to make AI systems work in practice: with users, data, constraints, and accountability for production outcomes.<h1 class="jobSectionHeader">
Key Responsibilities</h1><ul><li>
Build and operate AI systems deployed in customer environments, taking ownership of system behavior, reliability, and usefulness in production</li><li>
Design and implement compound AI workflows that combine models, prompts, agents, tools, retrieval, evaluation, feedback loops, and execution into coherent production systems aligned with user and SME needs</li><li>
Develop clean, maintainable Python services and application logic that integrate AI capabilities into customer workflows, data platforms, APIs, and existing applications</li><li>
Operate on live systems by measuring behavior, identifying failure modes, debugging issues, and iterating rapidly to improve quality, reliability, and user value</li><li>
Build evaluation frameworks, test cases, feedback mechanisms, and observability patterns that help teams understand and improve AI system performance over time</li><li>
Work directly with customer stakeholders and subject matter experts to understand workflows, clarify requirements, reason about tradeoffs, and adapt systems as needs evolve</li><li>
Use AI-native engineering tools to accelerate implementation, debugging, experimentation, data analysis, and system improvement</li><li>
Collaborate with other AI Engineers, AI Strategists, and other Distillers to make pragmatic system design decisions that balance speed, robustness, maintainability, and customer impact</li><li>
Take accountability for the production outcomes of the components, workflows, and systems you build</li></ul><h1 class="jobSectionHeader">
What We Require</h1><ul><li>
2+ years of software engineering experience</li><li>
Ownership mentality for AI systems. You take responsibility for whether the systems you build deliver their intended value in production. You are comfortable making technical decisions, learning from system behavior, and owning the results of your work</li><li>
Experience building AI systems. You have built applications powered by LLMs or other AI models and are comfortable composing multiple components &mdash; prompts, agents, tools, retrieval, evaluators, workflows, and integrations &mdash; into end-to-end systems. You reason about system behavior holistically rather than treating models as black boxes</li><li>
Strong engineering fundamentals. You write clean, maintainable Python and are comfortable building production software systems. You understand core engineering concepts like versioning, debugging, testing, performance, code review, and production readiness</li><li>
AI-native working style. You use AI tools daily to write and debug code, explore designs, analyze data, and automate repetitive work. You are curious about new model capabilities and techniques, and actively incorporate them into how you build and iterate on systems</li><li>
Comfort in customer environments. You are able to work directly with customer teams, ask good questions, and adapt quickly to new domains. You communicate clearly about system behavior, limitations, and tradeoffs, and can operate effectively in high-trust, high-visibility situations</li><li>
Pragmatic delivery mindset. You can navigate ambiguity, make progress with incomplete information, and balance speed with robustness when building systems that need to work for production users</li><li>
Willingness to travel. Travel is typically 10&ndash;30%, depending on the project, customer needs, and your role on the engagement</li></ul><h1 class="jobSectionHeader">
What We Offer</h1><ul><li>
The base salary range for this role is $150K &ndash; $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package</li><li>
100% covered medical, dental, and vision for employees and dependents</li><li>
401(k) with additional perks (e.g., commuter benefits, in‑office lunch)</li><li>
Access to state‑of‑the‑art models, generous usage of modern AI tools, and real‑world business problems</li><li>
Ownership of high‑impact projects across top enterprises</li><li>
A mission‑driven, fast‑moving culture that prizes curiosity, pragmatism, and excellence</li></ul>
Distyl has offices in San Francisco and New York. This role follows a hybrid collaboration model with 3+ days per week (Tuesday&ndash;Thursday) in‑office.
#LI-Hybrid
We believe diverse perspectives make our work stronger and more impactful. We are an equal opportunity employer and evaluate all applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other legally protected characteristic. We encourage candidates from all backgrounds to apply.</div>

Forward Deployed AI Engineer

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 an engineer at Notion, you&rsquo;ll help shape core user experiences and accelerate how people discover value in Notion. You'll tackle meaningful challenges with increasing autonomy, crafting code that millions of users will experience. You'll take ownership of projects that matter, make critical technical decisions, and contribute your unique perspective to our product vision. Working alongside passionate experts across design, product, and data, you'll help shape the future of how people work.
 
 
 We're looking for an New Grad AI Engineer to join as a strategic partner in shaping Notion's AI vision. You'll work on cutting-edge AI-powered features, leveraging LLMs, embeddings, and other AI technologies to make Notion more intelligent and capable.
 
 
 
 Role
 
 
 
 This role may be aligned to one of multiple AI-focused teams at Notion. Depending on team match and business needs, you could work on:
 
 
 <ul>
 <li>
 AI product engineering: building model-powered features end-to-end (UX, APIs, retrieval, orchestration, quality, and reliability)
 </li>
 <li>
 Model &amp; systems engineering: improving model integration and performance (latency, cost, safety, robustness) and building the infrastructure that supports model serving and experimentation
 </li>
 <li>
 Evaluation &amp; quality (evals): creating evaluation frameworks and automated/ human-in-the-loop testing to measure and improve model and product quality
 
 
 </li>
 </ul>
 What You&rsquo;ll Achieve
 
 
 <ul>
 <li>
 Partner with your team to prototype and ship an AI-powered product improvement
 </li>
 <li>
 Own a scoped productionization project: integrate a new model/technique into an existing workflow, add monitoring + guardrails, or improve latency/cost/reliability.
 </li>
 <li>
 Contribute to evals and iteration loops: build or extend an evaluation set, run experiments, analyze results, and translate learnings into product or system changes.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Pursuing a bachelor's or master&rsquo;s degree in computer science, engineering, or another related field. Must be able to start full time prior to July 27, 2026.
 </li>
 <li>
 Previous internship experience.
 </li>
 <li>
 Working towards a proficiency of one or more programming languages such as Typescript, React, Python, etc.
 
 
 </li>
 </ul>
 Skills You'll Need To Bring
 
 
 <ul>
 <li>
 Expertise building and prototyping: You&rsquo;re excited to build and iterate quickly, and you&rsquo;ve started exploring AI/ML through coursework, projects, internships, or hackathons. You&rsquo;re comfortable learning how different parts of a product fit together (UI, APIs, data), have some familiarity with relational databases like Postgres or MySQL, and can take an idea from prototype to a working feature with guidance.
 </li>
 <li>
 Problem-solving: You&rsquo;re curious and methodical. You ask good questions, break down ambiguous problems into smaller pieces, and iterate with feedback from teammates. You think about how what you build will impact users and the business.
 </li>
 <li>
 Communication and collaboration: You communicate clearly in writing and in meetings, and you&rsquo;re comfortable learning in public&mdash;sharing early work, asking for help, and incorporating feedback. You enjoy partnering with engineers and cross-functional teammates (product, design, data).
 </li>
 <li>
 Impact and user focus: You care about building the right thing, not just building it quickly. You prioritize work based on user needs and outcomes, and you&rsquo;re mindful that features at Notion can reach millions of people.
 
 
 </li>
 </ul>
 Nice To Haves
 
 
 <ul>
 <li>
 You have experience with any part of our technology stack: React, TypeScript, Node.js, Postgres, etc.
 </li>
 <li>
 You&rsquo;re a builder who finds joy in building software solutions for yourself or others
 </li>
 <li>
 You care about the interaction between technology and society, the ways in which they inform each other, and our responsibility as technologists to be conscious of that relationship.
 </li>
 <li>
 You've heard of computing pioneers like Ada Lovelace, Douglas Engelbart, Alan Kay, and others&mdash;and understand why we're big fans of their work.
 
 
 </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 and New York, the estimated base salary range for this role is $126,000-$146,000.
 
 
 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>

Software Engineer, New Grad (AI)

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

REVI

We're hiring a Junior AI Engineer to help us design, build, and ship agentic AI systems and ML-powered features into production. You'll work alongside senior engineers on real problems &mdash; building agents that take actions on behalf of restaurants, tuning models that personalize customer experiences, and integrating frontier LLMs into our product stack. This is a hands-on builder role; expect to write production code, deploy services, and iterate quickly based on user feedback.What You'll DoBuild and ship agentic AI features &mdash; single-agent and multi-agent systems that perform real tasks for restaurants and their customersDevelop deterministic ML models (classification, ranking, regression, retrieval) for use cases across the platformIntegrate LLM APIs (Anthropic Claude, OpenAI GPT, and others) into product workflows, including prompt engineering, tool use, structured outputs, and retrieval-augmented generationDeploy and maintain AI/ML services in production &mdash; containerized, observable, and reliableWork closely with senior engineers and product to translate fuzzy product ideas into shipped featuresContribute to evaluation and monitoring frameworks so we know our agents and models actually workWhat We're Looking ForBasic Qualifications0&ndash;3 years of experience building AI/ML systems. This can come from full-time work, internships, or substantive class/research projects &mdash; what matters is depth, not the sourceDemonstrable hands-on experience with agentic AI &mdash; single-agent or multi-agent systems built using frameworks like LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or custom orchestrationWorking knowledge of deterministic ML modeling &mdash; building, training, and evaluating models with libraries like scikit-learn, XGBoost, LightGBM, or PyTorchHands-on experience integrating LLM APIs (Claude, GPT, Gemini, etc.) into a working application &mdash; including prompt design, tool calling, and handling streaming/structured responsesSome deployment experience &mdash; you've put a service behind an API, containerized it, and run it somewhere real (AWS/GCP/Azure, Modal, Render, Fly, etc.)Strong Python skills; comfort with Git, REST APIs, and basic SQLBS or MS in Computer Science, Machine Learning, or a related quantitative field &mdash; or equivalent demonstrable skillPreferred QualificationsExperience with vector databases (Pinecone, Weaviate, pgvector, etc.) and RAG pipelinesFamiliarity with LLM observability/eval tools (LangSmith, Langfuse, Braintrust)Exposure to distributed data processing (Spark, Databricks) or streaming systems (Kafka, Kinesis, Pub/Sub) &mdash; even from an internship or courseworkExposure to voice AI, speech-to-text, or real-time conversational systemsExperience with restaurant tech, commerce platforms, or B2B SaaSOur ValuesHeart: A team that is passionate about what they do, with a heart of giving back.Impact: Being a versatile team player with an innovative mind and a firm backbone to make an impact on everything they touch.Excellence: A team committed to excellence in all we do, with integrity and supreme service.Perks and Benefits of Joining the Revi TeamShip real agentic AI to real customers &mdash; not demosWork directly with senior engineers and leadership on architecture and product decisionsMove fast, own outcomes, and grow rapidly into a senior ICCompetitive compensation, meaningful early-stage equity, and a strong AI-forward cultureExcellent and comprehensive health plans (Medical, dental, vision, etc)Flexible Vacation Policy, Paid holidaysOrganized volunteer events to give back to our communityOff-sites, events and happy hours401kComp range - Base : 130k USD - 170k USD + EquityPay: $130,000.00 - $170,000.00 per yearWork Location: In person

JR. AI ENGINEER

Explore 80+ Ai researcher in San Francisco this July. Find roles across top companies and apply now to boost your career.