Top AI & Machine Learning Jobs in San Francisco, CA

Conduct applied AI research and build foundation models from design through training, evaluation, validation, and deployment. Partner with data scientists, engineers, and product managers to deliver scalable AI products. Lead research initiatives involving large deep learning models, optimization, self-supervised learning, robustness, explainability, and fine-tuning. Translate research into production systems, develop platform or solution-level code, and guide long-term research agendas with measurable customer and business impact.

Leads applied AI research and strategic direction for foundation models and large-scale machine learning systems. Responsibilities include designing, training, evaluating, validating, and deploying models; pursuing novel research; collaborating with engineering, product, and data science teams; mentoring scientists and managers; translating research into business outcomes; and representing the company in the research community. Focus areas may include LLMs, behavioral models, optimization, fine-tuning, data preparation, and scalable production AI.

Develop and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, infrastructure utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineering staff.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research emerging methods, optimize large deep learning models, develop platform or solution code, publish findings, and pursue an independent research agenda focused on impactful customer applications.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Optimize AI systems for scalability, latency, throughput, hardware utilization, and cost. Define technical roadmaps and governance standards, lead design reviews, integrate heterogeneous AI systems, and mentor senior technical staff.

Build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML applications, distributed data pipelines, cloud-based infrastructure, model monitoring, retraining, CI/CD automation, testing, governance, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while optimizing algorithms and supporting large-scale containerized systems.

Design, develop, deploy, and support foundational AI systems, including LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term platform strategy, and mentor engineering colleagues.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, reliability, and hardware utilization. Own complex architectures, define reliability objectives, ensure security and compliance, influence technical roadmaps, and mentor engineering teams.

Conduct applied AI research and lead development of foundation models from design through production deployment. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research advanced methods, optimize training and inference, evaluate model performance, and translate research into production impact. The role also involves owning research agendas, publishing or advancing machine learning methods, leading cross-functional initiatives, and mentoring junior researchers or engineers.

Leads the design, development, deployment, and optimization of enterprise AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Establishes responsible AI standards, shapes long-term platform strategy, makes technology decisions, scales engineering practices, partners with research and risk teams, and leads multi-team engineering organizations. Mentors and develops talent while delivering scalable, secure, and compliant AI products.