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lead machine learning engineer lmts

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.
Lead member of technical staff - Machine Learning Engineering
Job Category: Software Engineering
Job Details
About Salesforce
We&rsquo;re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too &mdash; driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good &ndash; you&rsquo;ve come to the right place.
We are a foundation machine learning platform team within the Trust Intelligence Platform organization with a main focus to build and accelerate scalable and resilient machine learning pipelines across the security engineering organization.
We are looking for a highly motivated, hands-on lead machine learning engineer with a strong business understanding of cybersecurity problems, who acts as a force multiplier security data scientist for our security organization. The lead will not simply build models; they will architect the data-driven strategy for our threat detection capabilities.
Your impact:
<ul><li>Shape the Defense Strategy: You will own the decision-making process&mdash;translating vague security threats into concrete mathematical problems. By championing a rapid prototyping culture, you will validate hypotheses in days rather than months, ensuring our engineering resources are focused only on high-value detections while killing low-signal ideas early.
</li><li>Detect the &quot;Unknown Unknowns&quot;: You will lead the evolution of our threat detection, introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learing. Your work will expose sophisticated threats&mdash;such as active system intrusions, lateral movement, beaconing, and insider attacks&mdash;that evade traditional defenses, directly reducing the organization's risk surface.
</li><li>Elevate the Organization: You will act as a force multiplier, mentoring junior scientists and engineers, and building the internal tooling, feature stores, and libraries that make the whole team faster. You will influence the broader security engineering roadmap to ensure a closed loop security telemetry that is treated as a first-class citizen.
</li><li>Operationalize Intelligence: By prioritizing engineering rigor (CI/CD, scalable code) and adversarial resilience, you will deliver production-grade models that the SOC actually trusts&mdash;minimizing &quot;alert fatigue&quot; and maximizing analyst efficiency.
</li></ul>Required skills:
<ul><li>Extensive experience (3-5+ years) in data science, with at least 2+ years dedicated to the cybersecurity domain designing, implementing and deploying systems of anomaly detection, clustering, and graph models in production.
</li><li>Hands-on comfort with high-volume logs and proficiency with Spark/Pyspark, Snowflake, Flink and streaming services such as Apache Kafka
</li><li>Deep understanding and application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
</li><li>Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices.
</li><li>Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring.
</li><li>Solid foundation in feature engineering techniques and the implementation of feature stores.
</li><li>Experience in formulating ML governance policies and ensuring adherence to data security regulations.
</li><li>Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership.
</li><li>Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations.
</li><li>High degree of autonomy with the ability to look at a vague business problem and structure a data-driven solution without needing a predefined roadmap.
</li></ul>Preferred skills:
<ul><li>Masters or PhD in a quantitative field
</li><li>Expertise in advanced Natural Language Processing (NLP) methodologies.
</li><li>Experience contributing to open-source security data science tools.
</li><li>Presentations at major security conferences (Black Hat, DEF CON, BSides) or data conferences.
</li><li>Background in offensive security (Penetration Testing/Red Teaming) with an &quot;attacker's mindset.&quot;
</li><li>Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams.
</li><li>Previous experience in a mentoring role for junior engineers.
</li><li>Track record of publications and/or patents in quantitative disciplines.
</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 $172,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $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>

Lead Machine Learning Engineer - LMTS

<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

Apple

The Apple Services Engineering AI/ML organization is hiring a Machine Learning Engineer to join the Search team for Video catalog. 
 
Our team builds the core intelligence that powers search discovery experiences in the Apple TV App, Siri, and Spotlight cross platforms, helping users effortlessly find and enjoy the content they love. We are a collaborative, high-impact team that values innovation, craftsmanship, and end-to-end ownership from idea to launch. Our systems combine large-scale data, modern retrieval and ranking models, and a deep commitment to user privacy. 
 
Join us, you&rsquo;ll develop scalable systems and machine learning models that drive search relevance, personalization, and understanding of content at scale. Working closely with cross-functional partners in product and design, you&rsquo;ll translate cutting-edge research in advanced machine learning and generative AI into secure and delightful production features used by millions every day. 
 
Description 
 
As a Machine Learning Engineer on the Video Search team, within the Apple Services Engineering AI/ML organization, you will design and deploy large-scale ML systems that power search and discovery across Apple platforms. 
 
You&rsquo;ll apply machine learning, natural language understanding, and generative AI to model user intent and deliver relevant, personalized results. Your work will involve building and optimizing cutting edge data processing, ML models, retrieval pipelines, and ranking systems that operate at global scale and under strict privacy standards. 
 
This is a hands-on role where you will collaborate closely with cross-functional teams to bring advanced ML technologies into production-shaping how users discovery content they love in Apple TV app, cross Apple TV partners on Apple Platforms, also through Siri and Spotlight.&quot;,&quot;responsibilities&quot;:&quot;Solve complex research problems and implement solutions from concept to execution. 
 
Design and implement retrieval and ranking systems using semantics and user context. 
 
Build and deploy ML, NLP and LLM models to improve search relevance and personalization. 
 
Analyze data and model performance to identify opportunities for search quality enhancement. 
 
Develop automated tests for continuous integration and ensure successful production deployment. 
 
Conduct A/B tests to measure search improvements. 
 
Collaborate with cross-functional teams to innovate intelligent video catalog text search. 
 
Enhance search recall and ranking for global Apple devices across all platforms and languages. 
 
Utilize big data tech to evaluate content discovery features. 
 
Ensure systems meet Apple&rsquo;s privacy, efficiency, and user experience standards. 
 
Preferred Qualifications 
 
Experience with search or recommendation systems, and semantic retrieval or vector databases. 
 
Expertise in transformer architectures, embeddings, and retrieval or ranking models. 
 
Experience in applying or fine-tuning LLMs for understanding and generation tasks. Familiarity with prompt design, context management, RAG and Agentic architectures and solutions. 
 
Exposure to evaluation and safety frameworks for LLM-based systems. 
 
Knowledge of reinforcement learning and other modern post training practices for LLMs. 
 
Passion for developing intelligent, human-centered experiences to enhance content discovery. 
 
Minimum Qualifications 
 
Experience in machine learning, NLP, IR, or more recently Large Language Model ( LLMs). 
 
Strong programming skills in Python, Java and Go for building scalable ML systems. 
 
Hands-on expertise in ML libraries such as PyTorch, JAX, TensorFlow for model training and deployment. 
 
Ability to translate product goals into technical solutions, improving user experience. 
 
Strong communication, collaboration, and analytical problem-solving skills. 
 
In-depth knowledge of search and information retrieval fundamentals, including indexing and ranking. Experience with retrieval and ranking algorithms and building big data pipelines using Hadoop, Java, Scala, Spark and more. 
 
Industrial experience in search, classification, recommendation systems, or related fields. 
 
Familiarity with A/B testing and data-driven product development. 
 
Passionate about creating products loved by customers at Apple. 
 
Bachelor&rsquo;s degree or higher (or equivalent practical experience) in Computer Science, Machine Learning, Natural Language Processing, Artificial Intelligence, or a related field. 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Machine Learning Engineer, ASE Search Team

Quantcast

<div><div>At Quantcast, we don't just build advertising technology, we revolutionize how it works. Our AI-powered Demand Side Platform (DSP) connects the world's most ambitious marketers with their ideal audiences across the open internet, delivering results that actually move the needle. Since 2006, we've been the industry's trailblazer, launching the first AI-powered measurement platform for publishers and the first AI-driven DSP. Our AI doesn't just optimize&mdash;it delivers the measurable outcomes that matter most to our clients, giving them the competitive edge they need in a crowded marketplace. Ready to join the team that's defining the future of digital advertising?</div><div></div><div> 
The Modeling team is responsible for Machine Learning (ML) systems at Quantcast. We build and maintain multiple ML products that price millions of bid requests per second in a real-time auction environment to maximize advertiser outcomes. For each bid our models predict age, gender, viewability, fraud, advertiser relevance and many more characteristics. Using NLP, clustering and LLMs we build topics in multiple languages to help our advertisers target customers interested in relevant content.</div>
<div></div>
<div>As a Machine Learning Engineer you care about the health and maintainability of our systems and the velocity of the engineering teams. You explore data, research new algorithms, experiment with proof of concepts, and build out scalable real-time production systems to tackle challenges the company faces.</div>
<div></div>
<h3 class="jobSectionHeader">What you'll do:</h3><ul>
<li>Design, code, test, and debug ML applications and constantly improve large-scale global systems that respond to millions of real-time requests per second efficiently.</li>
<li>Run Machine Learning experiments to test new modeling ideas.</li>
<li>Write clean, efficient, and maintainable code using industry best practices.</li>
<li>Collaborate closely with engineering teams, to deliver high-quality ML products. Participate in code reviews and provide constructive feedback to team members.</li>
<li>Identify performance bottlenecks and optimize system components for enhanced scalability.</li>
<li>Keep up to date with developments in machine learning outside the company.</li>
</ul><h3 class="jobSectionHeader">Who you are:</h3><ul>
<li>New Grad to 1 year of experience.</li>
<li>You must be work-authorized in the United States without the need for employer sponsorship.</li>
<li>This is a hybrid role based in our San Francisco office. To ensure a manageable commute for in-office days, candidates must reside within a 60-mile radius of San Francisco, CA. No relocation candidates at this time.</li>
<li>Fluency in Python, Java or similar programming languages.</li>
<li>Strong skills in mathematics and statistics.</li>
<li>Working knowledge of ML algorithms such as classification, control systems, optimization, clustering, LLMs or recommendation systems.</li>
<li>An interest in distributed system and software design, concurrent algorithms, data structures, and software engineering.</li>
</ul><div>The expected base salary range for this role is $151,300 to $178,000.</div><div></div><div> 
At Quantcast, we craft offers that reflect your unique skills, expertise, and geographic location. On top of a competitive salary, this position includes a performance bonus, equity, and a comprehensive benefits package. For more details, visit our Careers Page and see how we support our team. We are headquartered in San Francisco with offices around the world. Quantcast is an Equal Opportunity Employer. Please see the Applicant Privacy Notice for details on our applicant privacy policy. Join the team that unlocks potential.</div></div><div></div>

Machine Learning Engineer

reddit

<div><div><div><div><div><div><div><div>Reddit is a community of communities. It's built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet's largest sources of information. For more information, visit www.redditinc.com.</div>
</div>
</div></div></div></div></div>Team overview:

The Engagement Modeling Team at Reddit focuses on building machine learning models to drive on-platform user engagement with diverse media and content, with a focus on predictive modeling to improve interactions of click-throughs and video view-throughs. This role offers a unique opportunity to shape and scale Reddit's Ads prediction models, in alignment with our product goals and driving SoTA modeling advancement.

This role is well-suited for a leader with deep machine learning expertise, strategic vision, and a collaborative mindset to engage with both technical and cross-functional stakeholders.

We're a remote-friendly company, and this position is open to candidates anywhere in the U.S.

Responsibilities:
<ul>
<li>Set Technical Vision and Strategy: Define and execute a roadmap for engagement modeling, balancing innovative modeling approaches with business objectives.</li>
<li>Drive Technical Execution: Oversee the model development lifecycle from ideation to deployment, ensuring high standards of ML performance and robustness.</li>
<li>Lead and Mentor a High-Performing Team: Recruit, mentor, and retain top ML talent, fostering a culture of growth, collaboration, and technical excellence.</li>
<li>Collaborate Cross-Functionally: Partner with PMs, data scientists, and other engineering teams to align on engagement strategies, data requirements, and model KPIs.</li>
<li>Innovate in ML Architecture: Implement and optimize model architectures tailored to engagement prediction, leveraging deep learning and advanced ML techniques.</li>
</ul>
Candidate Profile:

The EM will lead a diverse, high-impact team and will need to navigate and foster collaboration with various teams such as PM, DS, and engineering functions within Ads. Ideal candidates will have:
<ul>
<li>People Management Experience: Prior experience managing engineering teams with a strong emphasis on technical mentorship and team growth.</li>
<li>Set Technical Vision and Strategy: Ability to plan and execute a long-term technical strategy aligned with business objectives. Define and execute a roadmap for conversion modeling, balancing innovative modeling approaches with business objectives.</li>
<li>Drive Technical Execution: Oversee the model development lifecycle from ideation to deployment, ensuring high standards of ML performance and robustness.</li>
<li>Lead and Mentor a High-Performing Team: Recruit, mentor, and retain top ML talent, fostering a culture of growth, collaboration, and technical excellence.</li>
<li>Collaborate Cross-Functionally: Partner with PMs, data scientists, and other engineering teams to align on engagement strategies, data requirements, and model KPIs.</li>
<li>Innovate in ML Architecture: Implement and optimize model architectures tailored to conversion prediction, leveraging deep learning and advanced ML techniques.</li>
</ul>
Required qualifications:
<ul>
<li>At least 2+ of experience building and managing high-performing machine learning teams, ideally in the Ads domain. Will consider tech lead experience as well</li>
<li>Deep ML Expertise: Deep hands-on experience working with machine learning models and deploying them in large-scale production systems. Proven ability in training, evaluating, and deploying large-scale models.</li>
<li>End-to-End ML Lifecycle Experience: Proven ability in training, evaluating, and deploying large-scale models.</li>
<li>4+ years of hands-on experience with TensorFlow or PyTorch.</li>
<li>Strategic Thinking: Ability to develop and communicate a clear, compelling technical strategy that supports broader company objectives and addresses the needs of internal customers.</li>
<li>Impact-Driven Mindset: Passion for developing scalable, well-designed, and responsible AI solutions that drive business value.</li>
<li>Exceptional Communication &amp; Collaboration: Strong interpersonal skills and a collaborative mindset, with the ability to effectively communicate complex technical topics to diverse audiences and build strong relationships with cross-functional partners</li>
</ul><ul><li>Experience with Ads or Engagement Modeling is a plus.</li>
</ul>
Perks and Benefits:
<ul>
<li>100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago)</li>
<li>Competitive salary and equity options</li>
<li>Comprehensive health benefits (medical, dental, vision) &amp; workplace perks (home office set up stipend etc)</li>
<li>Generous 401k matching</li>
<li>Flexible vacation policy</li>
<li>Paid parental leave (4+ months)</li>
<li>Family planning support</li>
<li>Paid volunteer time off</li>
</ul>
#LI-AS1<div> 
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.</div></div>

Machine Learning Engineering Manager - Ads Engagement Modeling

Common Sense Media

<div>Data Scientist<div></div><div><div><div><div>
Organization 
Common Sense Media</div><div> 
Department 
Data</div><div> 
Reports To 
Senior Director, Data &amp; Analytics</div><div> 
Location 
San Francisco, CA</div><div><div> 
Classification 
Salary Range</div><div> 
Full-time, Exempt
$140,00&ndash;$166,250</div></div> 
</div></div></div>
POSITION OVERVIEW
Common Sense Media is seeking a Data Scientist to help build data capabilities that allows the organization to answer the questions that matter most to advancing our mission and driving impact for kids and families. This is a hands-on, full-stack individual contributor role responsible for developing pipelines, models, and analyses that turn data from across the Common Sense Media ecosystem into reliable, decision-ready insights.
The Data Scientist will develop predictive and statistical models that support organization-wide decisions, design and analyze experiments, apply causal inference methods, and lead impact analyses that measure how Common Sense Media's work affects kids, families, and educators, translating findings into clear, actionable recommendations for both technical and non-technical audiences. The Data Scientist's work will span the full range of data science at Common Sense Media, from foundational data engineering and data modeling through analytics, machine learning, experimentation, and impact analysis.
As a senior technical voice on the team, the Data Scientist will help uplevel the organization's capabilities in data science, machine learning, and generative AI, advising on methodology, helping to develop and test AI product features, and raising the technical bar within product and engineering.
KEY RESPONSIBILITIES
Analytics &amp; Modeling<ul><li>
Conduct rigorous exploratory and descriptive analyses that establish the foundational understanding of our audiences, programs, and performance.</li><li>
Develop predictive and statistical models that support organization-wide decisions.</li><li>
Apply modern machine learning techniques to ship models and analyses that drive impact.</li></ul>
Experimentation &amp; Causal Inference<ul><li>
Design and execute A/B tests and randomized controlled trials.</li><li>
Leverage rigorous quasi-experimental designs (e.g., difference-in-differences, regression discontinuity) and advanced causal inference techniques to evaluate the impact of complex product and program decisions where randomized testing is not feasible.</li></ul>
Impact Measurement<ul><li>
Lead rigorous impact analyses and program evaluations that measure how Common Sense Media's work affects kids, families, and educators.</li><li>
Apply causal inference and evaluation methods to measure real-world outcomes.</li></ul>
Insights &amp; Communication<ul><li>
Translate findings into clear, actionable recommendations for both technical and non-technical audiences.</li><li><h1 class="jobSectionHeader">
Partner closely with colleagues across Common Sense Media to put data to work across the organization.</h1></li></ul> 
Analytics Engineering<ul><li>
Own the data models that turn raw data into clean, reliable, analysis-ready data sets used across the organization.</li><li>
Work with internal teams and external partners to establish data collection approaches, integrate new data sources, and ensure that incoming data is structured and trustworthy.</li><li><h1 class="jobSectionHeader">
Build and maintain the data pipelines and transformations necessary to support modeling, reporting, and impact measurement.</h1></li></ul> 
Technical Leadership<ul><li>
Serve as a senior technical voice on the team, advising on methodology and raising the technical bar around how to use data in thoughtful decision-making.</li><li>
Help uplevel the organization's capabilities in data science, machine learning, and generative AI, supporting the development and testing of AI tools and features across the organization.</li></ul>
QUALIFICATIONS<ul><li>
8+ years of experience in applied data science, analytics, or a closely related quantitative field, with a track record of shipping models and analyses that have changed decisions.</li><li>
Strong hands-on skills in SQL and Python or R, and expert use of standard machine learning libraries.</li><li>
Experience building and maintaining production data pipelines using modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and modern data stack tooling (dbt, Airflow, Fivetran, or equivalents).</li><li>
Demonstrated experience designing and analyzing experiments, including A/B tests, quasi-experiments, and causal inference methods.</li><li>
Experience leading rigorous impact or program evaluation that measures real-world outcomes.</li><li>
Experience mentoring junior data scientists.</li><li>
Excellent written and verbal communication skills, with the ability to translate technical work into clear narratives for both technical and non-technical audiences.</li><li>
Demonstrated ability to serve as a senior technical voice within an organization, raising the technical bar of teammates and helping non-technical colleagues use data and AI tools effectively in their work.</li><li><h1 class="jobSectionHeader">
Strong understanding of data governance, privacy, and responsible data and AI practices, with particular sensitivity to data involving children and families.</h1></li><li><h1 class="jobSectionHeader">
Familiarity with product analytics, CRM, and marketing platforms (Amplitude, Google Analytics, Salesforce, Braze) and their underlying data structures is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience building predictive models for subscription, retention, lifetime value, or similar member or customer lifecycle problems is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience implementing and evaluating generative AI or LLM-based solutions, including prompt engineering, fine-tuning, retrieval-augmented generation, or self-hosted models, with rigorous evaluation against human or ground-truth baselines, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience designing AI evaluation frameworks, including human-in-the-loop evaluation, bias and fairness assessment, and ongoing monitoring of model performance, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Prior experience in a mission-driven, nonprofit, education, or media organization is a plus.</h1></li></ul>
WHAT WE OFFER<ul><li>
The opportunity to really make a difference in the lives of kids and families!</li><li>
A mission-driven team with a 23-year track record of impact for families and children.</li><li>
Competitive nonprofit compensation and comprehensive benefits.</li><li>
A collaborative, flexible work environment with meaningful leadership access and visibility.</li></ul>
ABOUT COMMON SENSE MEDIA
Common Sense Media is the leading nonprofit organization dedicated to improving the lives of kids and families by providing the research-backed information, education, and independent voice they need to thrive in the age of apps, algorithms, and AI. We rate, educate, and advocate for policies to protect and prepare kids online. Our ratings, research, and resources reach more than 150 million users globally, over 1.4 million educators, and more than 100,000 schools worldwide every year. Learn more at commonsense.org.</div>

Data Scientist

Google

<div>Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.<h3 class="jobSectionHeader">
Minimum qualifications:</h3><ul>
<li>Bachelor&rsquo;s degree or equivalent practical experience. 
</li>
<li>8 years of experience with software development. 
</li>
<li>7 years of experience leading technical project strategy, ML design, and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).</li>
<li>5 years of experience in a technical leadership role.</li>
<li>5 years of experience in a people management or team leadership role.</li>
<li>2 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal etc.) or with GenAI-related concepts.</li></ul> 
<h3 class="jobSectionHeader">
Preferred qualifications:</h3><ul>
<li>Master&rsquo;s degree or PhD in Engineering, Computer Science, or a related technical field. 
</li>
<li>5 years of experience working in a complex, matrixed organization. 
</li>
<li>Experience in building and deploying Agentic AI systems.</li>
<li>Experience in security domains.</li>
</ul><div><h2 class="jobSectionHeader">About the job</h2>
Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started - and as a manager, you guide the way. 
 
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally. 

Our team is at the forefront of revolutionizing Google Security Operations (SecOps) through the power of artificial intelligence. We are developing a Security Agentic Platform to enable the development of first-party and third-party security agents to deliver a proactive, intelligent, and automated Agentic Security Operations Center (SoC).
 
 
In this role, you will lead a team of talented engineers in building the next generation of Security Agentic AI platforms and systems that will protect major organizations worldwide.
Google Cloud accelerates every organization&rsquo;s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google&rsquo;s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
 
 
US: $262000 - $365000 (USD) + 25% bonus target + equity + benefits 
 
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader">
Responsibilities</h2><ul>
<li>Guide, mentor, and grow a team of engineers focused on developing and deploying agentic AI for security that will power the Google Unified Security products.</li>
<li>Manage the technical goal and strategy for the Security Agentic AI, ensuring alignment with the broader security teams.</li>
<li>Collaborate with cross-functional engineering teams along with product management, data science, and security engineering to define product requirements and roadmaps, and deliver features.</li>
<li>Oversee the end-to-end development lifecycle, from ideation and design to implementation, testing, and deployment of AI-driven agentic security solutions.</li>
<li>Foster a culture of innovation and continuous improvement, staying abreast of the latest advancements in AI, machine learning, and cybersecurity.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Senior Software Engineering Manager, Agentic Platform, Cloud Security

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