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

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

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.

Build and improve ML systems powering core AI capabilities: create and maintain evaluation datasets, design experiments, train and fine-tune models (including RL approaches), analyze results to drive product decisions, participate in research, and partner with product and backend teams to integrate models into production.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Lead vision and roadmap for large-scale recommender systems across Spotlight, Discover, and Friend Stories. Technically lead engineering teams, design next‑generation recommender and retrieval architectures, incorporate SOTA research and foundation models, advance ML infrastructure for scalability and reliability, and partner cross-functionally to drive experimentation, operational excellence, and cost-effective production deployments.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

The Principal AI/ML Engineer will develop advanced ML models for the AdTech platform, lead system design, and collaborate across teams to optimize advertising strategies using AI.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.

Design, build, and deploy core ML systems and components for an AI agent platform, including search ranking, proactive suggestions, orchestration, routing, and planning; integrate cutting-edge LLMs and ensure high-quality user experiences while working autonomously in a fast-paced, collaborative environment.

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.

Design, build, and deploy large-scale ML models powering core products; own full ML lifecycle from data analysis to production; partner cross-functionally to prototype and launch features; apply modern ML techniques and AI tooling while ensuring code quality, security, and scalable production services.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

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.

As an AI Engineer, you will develop and optimize AI systems for legal tasks, work on integrating models, and establish evaluation frameworks to enhance decision-making capabilities in complex legal workflows.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Design, train, and productionize underwriting ML models for real-time transaction decisions. Build feature pipelines and training datasets, run experiments, integrate models into batch/real-time systems, and monitor data and model health while collaborating with cross-functional teams.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Build and maintain production ML systems: training and inference pipelines, model packaging and serving, feature pipelines, monitoring for drift and degradation, automated retraining, and reproducibility/versioning across data and models.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.