Top Senior Machine Learning Engineer Jobs in San Francisco, CA

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Lead the architecture and implementation of a Kubernetes-native AI Factory platform for distributed training, simulation, evaluation, inference, and deployment. Build GPU infrastructure, scheduling, storage, networking, observability, model lifecycle capabilities, and self-service developer workflows across cloud, on-premises, sovereign, and air-gapped environments. Partner with ML researchers and autonomy teams while providing technical leadership on scalable, maintainable AI infrastructure.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Architect data-driven threat-detection strategies, translate complex security threats into mathematical problems, build probabilistic models and graph-analytics detections, and operationalize production-grade machine learning to minimize alert fatigue across the security organization.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

The Senior Compiler Engineer will develop a model compilation toolchain for deploying optimized machine learning models in autonomous vehicles, focusing on performance engineering and collaboration with cross-functional teams.

Design, build, deploy, and maintain production ML systems and data pipelines at scale. Lead cross-functional teams to implement ML architectures, ensure model performance and governance, automate CI/CD for models, and leverage cloud platforms and ML frameworks to deliver reliable, explainable solutions.

Develop and prototype large-scale text-to-image generative models (Pinterest Canvas), collaborate on multimodal embeddings, collect and prepare visual training data (including RLHF and fine-tuning), ship models to production, publish research, and mentor junior researchers.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

The role involves building production ML models for voice AI, focusing on fine-tuning, deploying, and evaluating models to enhance human-like interactions.

The Senior Machine Learning Engineer will design innovative machine learning models for drug discovery, leveraging large-scale single-cell datasets and collaborating with ML Scientists to develop optimized models.

Build and operate large-scale batch and streaming data pipelines and real-time feature infrastructure to support production ML. Develop ML-adjacent services, ensure reliability/observability, participate in on-call and incident response, and collaborate with ML, data, and platform teams to meet latency, availability, and data quality requirements.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Develop and execute ML technology stack vision for Ads, improve ML models, and enhance content understanding using LLMs in a collaborative environment.

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Design, implement, and deploy production-scale machine learning models to evaluate disputes, chargebacks, and fraud likelihood. Build and manage orchestration layers for multiple models, collaborate with stakeholders to integrate partner/customer data, and improve automated and supervised decision-making for a product-focused ML environment.

Own machine learning systems end to end, from data exploration and modeling through production deployment, monitoring, and iteration. Build scalable NLP, LLM, retrieval, ranking, recommendation, prediction, and agentic systems with explainable outputs. Partner with Product and Go-to-Market to deliver customer-facing AI capabilities, improve ML platform reliability, communicate technical work clearly, and mentor engineers.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

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.

Lead end-to-end ML model development for safety-critical driving behaviors, design experiments, improve data quality, deploy models and metrics fleet-wide, and communicate results across product and engineering teams.

Design, scale, and maintain low-latency, high-throughput ML inference infrastructure and feature stores; implement observability, validation, and automated model deployment; debug relevance systems; partner with product, data engineering, and Trust & Safety to deliver reliable, production-grade ML infrastructure.