Top AI Engineer Jobs in San Francisco Bay Area, CA

Develop machine learning models, predictive solutions, data pipelines, data models, and feature engineering workflows. Analyze large datasets, validate model outputs, document assumptions, and translate business needs into technical requirements and recommendations. Collaborate with stakeholders, refine analytical use cases, support solution prototyping, and guide junior team members through coaching and technical feedback.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

AI Engineer Interns will research, design, evaluate, and productionize AI-powered data capabilities. Responsibilities include running AI/ML experiments, developing agent self-improvement and continuous-learning systems, collaborating with product engineers, building evaluation and training infrastructure, optimizing performance, designing APIs, and contributing across the full development lifecycle.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agents, vector search, guardrails, evaluation, governance, and observability. Optimize models and infrastructure for cost, latency, throughput, scalability, and hardware utilization. Architect multi-model orchestration pipelines, establish AI cost-performance governance, lead technical design reviews, shape foundational AI roadmaps, and mentor senior AI engineering leaders.

Design, build, and deploy scalable AI/ML platforms and AI-powered solutions using patient data. Collaborate with product managers, data scientists, engineers, and stakeholders to deliver reliable systems through CI/CD, testing, cloud infrastructure, and quality gates. Contribute to architecture and technical strategy, evaluate emerging generative and agentic AI technologies, and promote responsible, maintainable, data-driven AI innovation.

Leads an AI engineering team building scalable, responsible generative AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and governance. Establishes technical strategy, optimizes production AI systems for cost and performance, manages cloud deployments, translates enterprise goals into roadmaps, and develops engineering talent.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Define technical standards for HPC, GPUs, Kubernetes, private and hybrid cloud, networking, storage, reliability, security, observability, and automation. Solve complex scalability and performance challenges, guide cross-functional initiatives, evaluate emerging technologies, mentor engineers, and embed governance, compliance, privacy, and responsible AI into secure, resilient infrastructure.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Define system architectures and SLOs, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.

Develop and improve reliable LLM and deep learning capabilities, build scalable real-time machine learning models, advance Responsible AI practices, and define technical strategies for deep learning, RAG, and LLMs. Collaborate with AI scientists and engineers, conduct ML and reinforcement learning research, fine-tune models, and drive adoption of AI best practices across service teams.

Develop and maintain enterprise AI/ML solutions using Generative AI, RAG, and agentic frameworks on AWS. Design scalable architectures with Terraform, build CI/CD pipelines using GitLab, automate deployments with Python and Bash, and develop React-based internal tools and APIs. The role also involves troubleshooting, reliability, governance, security, compliance, project collaboration, technical mentorship, and occasional production support or travel.

Designs, develops, deploys, and optimizes production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role establishes AI cost-performance standards, leads technical design reviews, contributes to platform roadmaps, optimizes model and hardware utilization, and mentors senior AI engineers while delivering scalable and responsible AI capabilities.

Designs, develops, deploys, and supports production AI systems, including foundation models, LLM inference, agentic workflows, guardrails, evaluation, and observability. Owns complex AI architecture, reliability objectives, governance, and optimization for cost, latency, throughput, and hardware utilization. Partners across engineering, research, product, and program teams; leads technical reviews; contributes to long-term platform strategy; and mentors senior technical associates.

Designs, develops, deploys, and supports foundational AI systems, including LLM inference, agentic workflows, vector search, guardrails, evaluation, and multi-model orchestration. Optimizes AI systems for cost, latency, throughput, scalability, and hardware utilization. Leads governance reviews and design councils, contributes to technical roadmaps, establishes responsible AI standards, and mentors senior engineering leaders.

Develop and operate large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, guardrails, model evaluation, vector search, memory, governance, and observability. Lead architecture and optimization for cost, latency, throughput, reliability, and hardware utilization. Define AI service objectives, ensure security and compliance, guide technical roadmaps, and mentor engineering teams.

Build and operate scalable generative AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluations, governance, and observability. Optimize AI systems for cost, latency, throughput, and hardware utilization; architect multi-model pipelines; establish performance governance and engineering standards; contribute to long-term platform strategy; and mentor senior AI engineers.

Design, develop, deploy, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Lead technical reviews and governance, define AI platform roadmaps, integrate heterogeneous AI systems, and mentor senior engineering talent.

Design, develop, deploy, and support large-scale generative AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Establish governance and engineering standards, lead design reviews, contribute to platform strategy, and mentor senior AI engineers.

Build AI-powered features and scalable agent infrastructure for Hex’s data analytics platform. Partner with research and product engineering teams on context, model prompting, experimentation tooling, and platform integration. Lead major product initiatives, develop performant web experiences, maintain high standards for testing and correctness, and mentor engineers. The role involves full-stack development across React, TypeScript, GraphQL, backend services, cloud infrastructure, and data systems.

Build and deploy AI capabilities for Hex’s Notebook Agent and context engine. Responsibilities include running experiments, fine-tuning models, developing AI infrastructure and experimentation tooling, architecting AI-enabled systems, and shipping production experiences for data scientists and analysts. The role partners closely with product teams, spans greenfield initiatives, and offers opportunities for technical and team leadership.

Lead the architecture, development, and production deployment of advanced AI systems for risk management and compliance. Design agentic frameworks, LangGraph workflows, distributed memory infrastructure, model-serving platforms, PEFT optimization, and AI observability solutions. Mentor engineers, establish technical strategy, collaborate across teams, and evaluate emerging GenAI, LLM, and NLP technologies. The role also requires integrating AI APIs and microservices with banking applications while ensuring scalability, reliability, compliance, and operational excellence.

Design, develop, deploy, and optimize production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration and cost-performance optimization across cloud infrastructure. Establish AI engineering standards, guide technical roadmaps, lead design reviews, and mentor senior AI engineers while integrating research advancements into scalable, responsible products.

Build and deploy AI-native products that transform large-scale geospatial data into actionable intelligence. Design multi-agent workflows, integrate frontier LLMs, engineer tool-using agents, and develop data pipelines for anomaly detection and predictive analytics. Ensure secure, scalable cloud integrations and operate production AI systems using DevOps, containerization, orchestration, and CI/CD. Establish feedback and reinforcement mechanisms to improve model reliability while contributing to AI-first engineering standards and documentation.

Design and deploy production AI assistants, copilots, agents, RAG systems, and workflow automation using enterprise data. Build AI backend services, APIs, microservices, and AWS serverless workflows; integrate enterprise platforms; implement observability, responsible AI, human-in-the-loop controls, and traceability. Partner with Data, IT, and business teams to improve operational processes and decision-making while managing production incidents, model rollbacks, performance, accuracy, and cost.

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Develops and transfers AI research into real-world autonomous aircraft and vehicle systems. Evaluates multimodal perception models, builds prototypes, combines learned and classical computer vision methods, and optimizes capabilities for edge deployment. The role involves Python and C++ software, sensor fusion, model evaluation, robotics, and collaboration with researchers and autonomy engineers to deliver tested, documented production capabilities.