Top Senior Machine Learning Engineer Jobs in San Francisco, CA

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

The Senior Machine Learning Engineer will design, implement, and scale advanced foundation models, collaborate with researchers, and build AI systems. Requires hands-on experience with distributed training pipelines and proficiency in AI frameworks.

Design, build, and maintain ML infrastructure and data pipelines; develop, deploy, and monitor underwriting and financial-service models in production; run A/B tests and experiments; collaborate with data scientists, engineers, and product to integrate models; mentor junior engineers and implement continuous improvement for model performance and scalability.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

As a Senior Machine Learning Engineer, you will design and maintain ML systems for content recommendations, search, and spam detection, while augmenting user experience and system performance.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

The Senior Machine Learning Engineer will automate healthcare compliance workflows using AI to process unstructured data, build multimodal AI pipelines, and develop extraction systems, ensuring high reliability and operational efficiency.

The Senior Machine Learning Engineer will design, implement, and deploy ML models, contribute to engineering standards, and mentor junior engineers.

Develop and train computer vision and multimodal models, design scalable AI frameworks, and improve systems through fine-tuning and reinforcement learning techniques while collaborating with multiple teams.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Design and optimize pre-training pipelines for VLM models, conduct research on pre-training techniques, and evaluate performance for autonomous driving.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and own production-grade LLM and agentic AI systems, including evaluation pipelines, failure diagnosis, retrieval and orchestration, data-driven improvement loops, deployment, and observability while collaborating with clinicians and product teams.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

Develop end-to-end perception solutions for autonomous driving, leveraging deep learning and data-driven approaches to enhance system accuracy and efficiency.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Build and deploy computer vision and machine learning perception systems for a distributed robotic fleet. Responsibilities include object detection, depth estimation, localization, simulation and sim-to-real validation, vision data pipelines, camera and ToF calibration, on-device model optimization, and integration with motion and safety systems. The role also includes technical leadership, design documentation, code reviews, mentoring, and cross-functional collaboration with robotics, sensing, software, controls, and safety teams.

As a Senior Software Engineer in Robotics, you will transition Perception tasks to machine learning, focusing on system integration and optimization.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.