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

Architect and ship large-scale ML and evaluation systems for Atlassian Rovo Chat. Define technical strategy for measuring helpfulness, correctness, reliability, and agentic capabilities; develop datasets, simulations, automated judges, experimentation platforms, and production quality signals. Identify failure modes, implement interventions, and measure customer impact. Lead cross-functional initiatives, establish reusable AI quality standards, and mentor engineers while balancing quality, safety, latency, reliability, and cost.

Build, deploy, and improve production machine learning systems, including models, data pipelines, AI services, and monitoring workflows. Responsibilities include fine-tuning and evaluating models, designing scalable training and inference infrastructure, optimizing performance and cost, integrating APIs and internal tools, and partnering with product, research, and design teams to deliver reliable features. The role requires hands-on software delivery experience, cloud knowledge, and practical application of machine learning or analytics-heavy systems.

Build and lead large-scale, real-time machine learning systems for Pinterest’s growth content and notification ranking. Drive model improvements through A/B testing, own training and serving infrastructure, establish evaluation and monitoring practices, and guide technical direction across teams. Mentor engineers, conduct rigorous reviews, collaborate cross-functionally, apply state-of-the-art research, and improve engineering quality, system reliability, latency, and cost efficiency.

Develop and productionize deep learning models across the full machine learning lifecycle, including neural network design, data preparation, training, tuning, deployment, monitoring, and continuous improvement. Build scalable data pipelines for large datasets, collaborate with Backend, DevOps, and data-labeling teams, and apply security and information-protection practices. The role requires strong Python and machine learning framework expertise, production deployment experience, and familiarity with distributed data technologies.

Leads end-to-end machine learning development, including neural network design, data collection, model training, tuning, deployment, monitoring, and optimization at production scale. Builds scalable code and data pipelines, develops novel ML algorithms, improves backend systems and engineering processes, conducts metric-driven research, mentors engineers, and contributes to product strategy and roadmap planning.

Build and deploy machine learning systems for ransomware detection, suspicious sessions, user behavior analytics, and anomaly detection. Own high-volume ML pipelines using Spark, GCP Dataflow, and Vertex AI; develop feature stores; operate production models; and collaborate with platform, engineering, and product teams. The role includes on-call support and requires strong Python, cloud ML, MLOps, model evaluation, and production deployment experience.

Develop and maintain data integration, ETL, ML, and monitoring pipelines supporting AI/ML and generative AI tools in UCSF Health’s research infrastructure. Productionize models, implement data integrations, design monitoring metrics for safety, bias, drift, performance, and validity, and collaborate with researchers, data scientists, and technical teams. The role requires advanced Python, SQL, MLOps, CI/CD, cloud architecture, and Epic Clarity/Caboodle data-model expertise.

Owns the production ML and GenAI platform, including training and inference pipelines, model serving, registries, monitoring, evaluations, agent gateways, observability, cost controls, and incident response. The role builds reusable deployment patterns, governs agent tools and access, partners with Cloud Engineering, Data Engineering, Security, and SRE, maintains platform architecture, and mentors engineers. Candidates need strong Python and SQL, production ML operations experience, cloud platform expertise, and familiarity with LLM, agent, and MCP architectures.

Architect and build a multi-cloud ML training platform for decentralized large-model training and inference across consumer devices and cloud instances. Responsibilities include infrastructure orchestration, distributed compute, GPU workloads, checkpointing, data streaming, network-condition simulation, node-churn management, observability, reliability, and resilient job execution. The role requires strong Python, infrastructure-as-code, Kubernetes, distributed systems, decentralized networking, and SRE experience.

Owns the infrastructure and production lifecycle for product and marketing machine learning models. Responsibilities include real-time model deployment, feature pipelines and stores, monitoring and alerting, CI/CD, retraining infrastructure, marketing model operationalization, data pipelines, ad-platform feedback loops, and dependency management. The role bridges data science and engineering while mentoring teammates and making pragmatic architecture decisions.

Researches, designs, develops, trains, and deploys computer vision and machine learning models. Builds and maintains ML software, testing and validation processes, and documentation. Applies image and video processing, deep learning, and computer vision techniques across edge and cloud-based platforms while improving existing applications and performance.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

Build and operate enterprise AI integration products, including MCP servers, agent-facing APIs, React and TypeScript experiences, semantic retrieval systems, and automated AI quality evaluations. Improve latency, reliability, token efficiency, observability, and security across distributed systems. Lead cross-functional engineering initiatives, mentor engineers, and implement enterprise safeguards such as tenant isolation, audit logging, OAuth, and prompt-injection defenses.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

Build and operate scalable machine learning systems for Atlassian’s Growth organization, including personalized recommendations, ranking, decisioning, experimentation, and customer journey optimization. Partner with product, engineering, data science, analytics, marketing, and sales teams to translate growth opportunities into measurable production capabilities. Develop datasets, features, models, evaluation frameworks, and monitoring systems while optimizing for customer value, fairness, quality, and long-term business outcomes.

Build and operate scalable machine learning systems for personalized recommendations, growth journeys, cross-product expansion, and sales experiences. Design models, datasets, features, evaluation frameworks, and decisioning services; run offline policy evaluations and online experiments; and monitor attribution, latency, quality, and fairness. Partner with product, engineering, data science, analytics, marketing, and sales teams to turn ambiguous growth opportunities into measurable production capabilities and strategic ML initiatives.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Lead the design, development, deployment, and optimization of production machine learning systems for search, recommendations, personalization, NLP, deep learning, and AI applications. Own projects across the full ML lifecycle, design scalable architectures and evaluation strategies, collaborate with product and engineering teams, translate research into reliable systems, and mentor other engineers.

Develop a modular robot autonomy stack combining Vision-Language-Action models with manipulation modules. Build safety layers, data pipelines, model-agnostic interfaces, and high-performance inference software for production robot fleets. Advance dexterous manipulation through reinforcement learning, imitation learning, and novel research while supporting real-world customer applications.

Lead the technical vision and long-term machine learning strategy for Consumer Incentives. Design personalization, causal measurement, experimentation, and constrained optimization solutions; architect scalable end-to-end ML systems; evaluate reinforcement learning, foundation models, and generative AI; and drive cross-functional initiatives from formulation through production. Mentor technical leaders, establish engineering best practices, and connect model improvements to measurable consumer and business outcomes.

Lead the technical direction for Uber’s rider pricing machine learning systems. Develop and improve pricing and promotion-targeting models, optimization algorithms, and production serving systems. Own technical roadmaps, collaborate cross-functionally, mentor engineers, and lead teams delivering scalable real-time and offline solutions. Apply deep learning, generative AI, causal modeling, personalization, and reinforcement learning to improve ridership, revenue, and profitability.

Own the machine learning model lifecycle from experimentation through production deployment. Train and evaluate generative image and video models, design evaluation suites, build curated datasets from user activity and expert feedback, and develop compact in-house models optimized for speed and quality. This individual contributor role requires ownership of modeling work in a small team and practical experience delivering generative or computer vision systems.