Top Machine Learning Engineer Jobs in San Francisco Bay Area, CA

Build and deploy machine learning models for identity, fraud, and credit risk; research solutions, develop scalable modeling frameworks, integrate LLMs, monitor model performance, and collaborate with Engineering, Product, Design, and Risk stakeholders. The role also requires communicating findings to nontechnical audiences and identifying opportunities to use data to improve products and risk management.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring and retraining models, and applying responsible AI practices. The role collaborates with Product and Data Science teams and uses Python, Java, Scala, or related languages.

Develops end-to-end AI features for Atlassian’s developer AI products, including LLM integration, prompt engineering, machine learning algorithms, data preprocessing, feature engineering, experimentation, evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and partner teams to solve complex problems and apply advances in AI and machine learning.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Build AI agent orchestration, routing, planning, memory, and action systems. Develop and deploy machine learning models for search ranking and proactive suggestions, integrate modern LLMs, and design evaluations to improve quality, speed, and cost. The role requires independent ownership, strong product sense, and collaboration in a fast-paced environment.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

Leads Snap’s Content Machine Learning Engineering organization, setting multi-year strategy for recommendation, ranking, content understanding, reliability, and experimentation. Oversees distributed teams, develops organizational roadmaps, partners with executives and cross-functional leaders, influences scalable architecture decisions, and builds high-performing engineering teams. The role requires strong expertise in machine learning, deep learning, AI, ranking systems, and large-scale engineering leadership.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.

Leads machine learning engineering teams while designing, deploying, scaling, monitoring, and governing production ML models and platforms. Responsibilities include building distributed ML systems, cloud architectures, data pipelines, CI/CD automation, model retraining, responsible AI practices, and resilient production services. The role combines hands-on technical leadership with mentoring, recruiting, talent development, and cross-functional collaboration with Product and Data Science teams.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.

Own the architecture and evolution of core Weights & Biases ML workflow systems, including Artifacts, Registry, Automations, and Launch. Design scalable services, optimize complex systems, lead migrations and re-architecture, reduce technical debt, and improve reliability. Work across Go backends, GraphQL APIs, Python SDKs, and TypeScript frontends while collaborating with product, design, customers, and ML platform teams. Mentor engineers and establish engineering best practices for high-volume, production AI infrastructure.

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

Develop and scale machine learning algorithms, models, infrastructure, and data pipelines supporting AI features across Atlassian products. Responsibilities include designing model and system architectures, conducting experimentation and evaluations, collaborating with product, engineering, and analytics teams, building production-quality solutions using large datasets, and mentoring junior ML engineers.

Build and fine-tune foundation, ranking, CTR, conversion, and engagement models. Own the complete model lifecycle from research and experimentation through training, evaluation, deployment, monitoring, and continuous improvement. Develop scalable ML infrastructure, feedback loops, and evaluation systems tied to customer outcomes. Collaborate with technical and non-technical teams to translate business goals into modeling objectives and product improvements.

Research, implement, and optimize machine learning methods for high-fidelity 3D simulation and autonomous vehicle development. Responsibilities include training generative and world models, building scalable cloud pipelines, improving rendering and data-generation tooling, and integrating ML techniques into production simulation systems. The role collaborates with researchers, technical artists, and simulation engineers and focuses on 3D vision, reconstruction, radiance fields, and rare-scenario generation.

Develops and optimizes machine learning models for realistic 3D simulation, including 4D generative and world models for rare autonomous-driving scenarios. Responsibilities include researching 3D vision methods, building scalable cloud pipelines, integrating ML techniques into production simulation, and improving rendering and data-generation tools. The role requires experience with Python or C++, radiance fields, 3D or video generative models, 3D reconstruction, human-centric vision, and probabilistic techniques.

Develops agentic systems and machine learning models using frontier, genomics, and multiomics models to advance disease understanding, drug design, therapeutic response, and longevity research. The role involves building and shipping high-performing AI/ML products, applying engineering best practices, evaluating models, and collaborating across teams. Genomics and RNA biology experience is helpful but not required.

Develops core AI capabilities for Atlassian’s DevAI products, including LLM-based methodologies, prompt engineering, end-to-end AI features, data preprocessing, feature engineering, experimentation, model evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and cross-functional partner teams to solve complex problems and apply advances in machine learning and AI.

Lead the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.