Top Senior Machine Learning Engineer Jobs in San Francisco, CA

As a Senior Machine Learning Engineer, you will drive AI vision, develop models, and collaborate cross-functionally to enhance user experiences.

Design, build, and productionize ML systems focused on information extraction, retrieval, ranking, and recommendation. Own full ML lifecycle from feature engineering to deployment, observability, and evaluation. Collaborate with data, infra, and product teams to scale serving for streaming and batch inference and improve models using dataset engineering and graph/embedding techniques.

Lead design and deployment of production-grade agentic AI systems for healthcare. Architect scalable multi-agent orchestration, data curation pipelines, evaluation frameworks, and translate ML research to secure, low-latency clinical applications while mentoring engineers and collaborating with clinical and product teams.

Design, build, deploy, and operate production-grade ML systems and pipelines for a massive consumer audience. Own full lifecycle from data processing and feature engineering through training, validation, deployment, and monitoring. Drive ML infrastructure and architecture decisions, partner with product and data teams, optimize models against business metrics, and mentor engineers to ensure security, reliability, and compliance.

As a Senior Machine Learning Engineer, you will design and operate scalable ML systems for real-time personalization, collaborating with teams and mentoring engineers.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

As a Machine Learning Engineer, you'll build ML infrastructure, develop scalable data pipelines for farm images, and collaborate with farmers and agronomists to apply ML solutions.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

As a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

This role involves optimizing model serving layers for voice AI applications, working with inference engines, and improving performance for STT and TTS systems.

Lead efforts in Object Detection and Tracking by architecting deep neural networks, adapting research papers into production solutions, and optimizing systems for real-time performance.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Design, build, and deploy scalable distributed deep learning pipelines and infrastructure. Optimize hardware utilization, profile and benchmark training, manage large datasets, and enable reproducible ML workflows in cloud environments. Collaborate with ML scientists and engineers to align pipelines with research goals and drive performance, reliability, and operational best practices for large-scale model development.

Design and operate ML training and serving infrastructure, build a Kubernetes+Ray backend, improve developer experience and observability, mentor engineers, and ensure reliable, secure deployments for low-latency, high-performance systems.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

Design and develop auto-labeling algorithms for autonomous driving, collaborating with ML engineers to enhance data quality and efficiency in model training.

Build and optimize Swayable’s AI, machine learning, scientific computing, and data analytics engine. Responsibilities include Python software development, scalable distributed and parallel computing architecture, vectorized high-performance code, machine learning tools, data visualization, testing, maintenance, and collaboration with engineers, data scientists, and researchers. The role also involves solving novel technical problems, improving AI/ML operations, mentoring junior engineers, and documenting implementation tradeoffs.

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

The role involves managing ML infrastructure, building scalable data pipelines, operating training frameworks, leading projects, and ensuring best DevOps practices for machine learning.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

As a Senior Machine Learning Engineer on the Perception Mapping team, you'll develop ML models for semantic map detection, integrate them into autonomy systems, and lead technical efforts in model validation and best practices.