Top AI & Machine Learning Jobs in San Francisco, CA

Leads AI engineering teams responsible for foundational AI infrastructure, including model access, LLM inference, optimization, guardrails, evaluation, governance, observability, and cost management. Oversees development and deployment of scalable AI systems, makes build-versus-buy decisions, establishes technical vision, applies advanced research, and develops high-performing engineering talent.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Partner with data scientists, software engineers, ML engineers, and product managers to deliver scalable AI products. Research areas include deep learning, graph and sequential models, model optimization, self-supervised learning, robustness, explainability, RLHF, LLM fine-tuning, and data preparation. Own research agendas, publish innovative work, and translate technical findings into business outcomes.

Lead development of scalable, responsible AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize AI training and inference for scalability, cost, latency, throughput, and hardware utilization. Partner with engineers, scientists, technical program managers, and product managers to deliver production AI systems and shape the long-term technical roadmap.

Lead a cross-organizational program to transition engineering to an AI-driven, self-optimizing ecosystem with robust telemetry and evaluation. Orchestrate strategy and execution, mitigate technical risk, build and grow a TPM practice, align product/engineering/design/data science, and deliver measurable business impact.

Design, develop, deploy, and support scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production AI systems for cost, latency, throughput, and scalability. Contribute to foundational AI strategy and roadmap while collaborating with engineering, research, product, and program teams. Lead and mentor engineers and apply emerging AI research to responsible production systems.

Leads the design, development, and productionization of machine learning systems at scale. Builds and validates models, data pipelines, cloud-based ML architectures, and application code; automates testing and deployment; monitors and retrains production models; and ensures high availability, performance, security, governance, and responsible AI practices. Collaborates with Product, Data Science, and Agile engineering teams to deliver optimized ML applications.

Design, build, deploy, scale, and monitor production machine learning systems. Develop models, application code, data pipelines, cloud architectures, automated tests, and CI/CD processes. Collaborate with Product and Data Science teams, ensure high availability and performance, govern models for risk and responsible AI, and lead teams developing ML solutions.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for performance, scalability, latency, throughput, hardware utilization, and cost. Shape the technical vision and roadmap for foundational AI platforms while collaborating with engineers, research scientists, technical program managers, and product managers. Lead technical innovation and influence cross-functional stakeholders.

Lead data engineering role responsible for designing, developing, testing, implementing, and supporting cloud-based data solutions. The position uses Python, Java, Scala, SQL, distributed computing, streaming technologies, data warehouses, NoSQL databases, and public cloud platforms. Responsibilities include collaborating with Agile and product teams, building scalable systems, conducting code reviews and unit tests, optimizing performance, tracking technology trends, and mentoring engineers.

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.