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

Design and implement AI systems and scalable machine learning models, perform data wrangling and software engineering to deploy AI solutions, interpret data for client recommendations, collaborate across teams, uphold technical standards, and build client relationships while navigating complex, ambiguous problems.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

Lead modelling and analysis roadmap for FX products: forecast and monitor FX volatility, optimise trade execution with statistical/ML models, run customer and profitability analyses, and partner with product, trading, risk, and engineering to translate insights into trading and product decisions.

Design, build, and deploy large-scale machine learning models powering Snapchat features. Own full ML lifecycle from data analysis to production, prototype with cross-functional partners, apply modern ML techniques, and use AI tools while ensuring code correctness, security, and scalable system design.

Lead research, development, and deployment of recommendation and personalization ML models. Design algorithms, build end-to-end pipelines, optimize for accuracy/latency/scale, integrate solutions into production, implement monitoring/bias detection/retraining, and collaborate on experimentation and product integration to improve user engagement and retention.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Lead and grow the Machine Learning Engineering team, set technical direction, define team roadmap, and collaborate across product and engineering teams.

Design and implement ML algorithms for bidding and ranking systems in ads. Own complete ML systems from conceptualization to production. Collaborate with teams to integrate models and enhance advertising value using AI.

Design, train, and deploy ML models and pipelines to optimize Databricks serverless compute. Build end-to-end ML4Sys solutions, scale infrastructure, implement optimization and scheduling algorithms, and collaborate on product roadmap to improve performance and cost efficiency.

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

Lead roadmap and product strategy for vehicle autonomy. Define autonomy behavior, UX, data and ML lifecycle, benchmarks, and deliver cross-functional capabilities from data collection through deployment and monitoring.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead modeling of weather patterns and their impact on the Waymo Driver. Build pipelines and spatio-temporal models integrating multiple weather data sources. Measure performance in adverse weather, develop scalable analysis frameworks, optimize operational mitigation processes, and collaborate with product and engineering. Act as a technical contributor and lead, scope ambiguous problems, and champion measurement rigor across teams.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

Develop and implement advanced reinforcement learning methods and distributed systems for AI agents. Collaborate with customers to understand needs and create tailored solutions.

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.