Top AI Engineer Jobs in San Francisco Bay Area, CA

Build and operate backend services powering Mercor’s Applied AI platform, including APIs, data models, pipelines, background jobs, and containerized infrastructure. Own features end to end from design through production operations. Improve throughput, reliability, observability, cost, and recovery across distributed systems. Use Terraform, workflow orchestration, monitoring, and incident response while collaborating with product, operations, and research teams.

Lead LiteLLM’s unified API team, overseeing model, endpoint, and provider integrations across 100+ LLM providers. Design provider-agnostic gateway features, manage open-source issues and pull requests, review AI team code, guide product priorities through customer feedback, and own developer experience for the Python SDK and proxy. This hands-on technical leadership role requires deep LLM API expertise, strong Python engineering, customer empathy, and the ability to establish technical standards.

Develops agentic systems and machine learning models using frontier, genomics, and multiomics models to advance disease understanding, drug design, therapeutic response, and longevity research. The role involves building and shipping high-performing AI/ML products, applying engineering best practices, evaluating models, and collaborating across teams. Genomics and RNA biology experience is helpful but not required.

Architect and build Traba’s AI agent platform, including orchestration, model strategy, retrieval, memory, tool integrations, evaluation, observability, and deployment infrastructure. Lead foundational technical decisions, customer-field discovery, and integrations with WMS, TMS, and ERP systems. Establish quality and reliability standards, build evaluation and experimentation systems, partner with leadership on the platform roadmap, and hire and mentor engineers.

Build and ship AI-powered product features across Traba’s staffing marketplace, including automated worker vetting, matching, fulfillment, and business workflows. Lead frontend and backend development, architect real-time matching and distributed systems, integrate LLMs and agents, develop APIs, and own product surfaces end to end. Mentor engineers and partner with product, design, and the CTO on architecture and roadmap.

Build and deploy production AI agent systems for industrial supply chain workflows. Responsibilities include agent harnesses, tool use, sub-agents, retrieval, structured outputs, MCP servers, orchestration, evaluation frameworks, prompt engineering, backend services, APIs, data models, and integrations with customer WMS, TMS, and ERP systems. The role partners with customers, operators, product, and engineering teams to develop and scale reliable agent solutions.

Build and scale backend systems for production LLM applications in healthcare. Responsibilities include architecting APIs, services, data and RAG pipelines, LLMOps observability, performance optimization, cloud infrastructure, security, HIPAA-ready compliance, and auditability. The role partners with product, ML, and healthcare teams while leading delivery, establishing engineering practices, and mentoring engineers. Candidates need strong Python and backend engineering skills, distributed systems expertise, cloud-native experience, and at least three years working with ML or LLM applications.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

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 and build production AI services and agent experiences for consumer and business users. Own features from system design through deployment, incorporating user experience, evaluation, monitoring, safeguards, and guardrails. Collaborate with product, design, and engineering teams to deliver high-impact AI functionality. Develop RAG pipelines and tool-using language model systems, improve AI-assisted development practices, and build reliable web applications for fintech and payments use cases.

Applies AI and machine learning techniques to product specifications, recommendations, and agentic workflows. Responsibilities include prototyping, evaluating, and iterating on models and AI-powered customer features; analyzing structured and unstructured data; translating customer problems into technical approaches; and documenting results. The intern collaborates with Applied AI Engineers, Product Managers, and Engineering while gaining experience shipping practical AI systems.

Design and build Databricks Model Serving infrastructure for scalable, low-latency CPU and GPU inference. Lead architecture, technical roadmaps, performance optimization, autoscaling, routing, caching, observability, deployment workflows, and operational excellence. Collaborate with product, platform, infrastructure, and research teams, drive high-impact initiatives, establish engineering best practices, and mentor engineers while influencing the broader AI platform strategy.

Design, build, evaluate, debug, and deploy AI systems from prototypes to reliable enterprise production. Partner with educational institutions to define architectures, integrations, evaluation strategies, and success criteria. Address model behavior, agents, retrieval, reliability, observability, security, governance, latency, cost, and safety while guiding customer adoption and translating deployment lessons into reusable architectures and product feedback.

Build reproducible data and AI workflows using Python, SQL, notebooks, and version control. Prepare and validate structured and unstructured data, support analytics and machine learning, prototype generative AI applications, evaluate models, and contribute to applied R&D. Communicate assumptions, limitations, and findings to technical and consulting stakeholders while developing reusable tools, documentation, and training materials in a collaborative consulting environment.

Build the application and cognitive layer for humanoid robots, translating speech, gestures, and contextual inputs into robotic actions. Develop behavior trees, state machines, voice applications, multimodal interaction systems, agentic AI workflows, and integrations with robotics capabilities. Work across LLMs, VLMs, perception, edge/cloud systems, telemetry, testing, observability, CI/CD, and production deployment for human-robot experiences.

Build and operate infrastructure for Databricks AI products, including MLflow, AI Gateway, agent frameworks, model serving, and foundation model APIs. Improve the reliability, latency, and efficiency of distributed AI workloads while collaborating with platform, infrastructure, and machine learning teams. The role also involves shaping developer and data scientist experiences and requires strong backend engineering, scalable systems, cloud infrastructure, and ownership skills.

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.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Lead the architecture, research, evaluation, and production deployment of agentic AI and LLM systems for healthcare applications. Build multi-agent orchestration, memory, tool-use, fine-tuning, prompt optimization, evaluation, and data curation pipelines. Collaborate with clinical, product, infrastructure, and software teams to deliver secure, low-latency systems, while mentoring engineers and establishing technical standards.

Lead the design and operation of scalable machine learning infrastructure, including data generation, distributed training, production serving, evaluation consistency, accelerator optimization, reliability, and observability. Set technical direction, guide architecture, mentor engineers, and collaborate across modeling, evaluation, and platform teams. Build systems supporting autonomous driving models, simulation data, and large-scale production inference.

Own the design, roadmap, security, observability, and production operations of Tinder’s internal AI agent platform. Build gateways, MCP connectors, identity and delegation systems, fine-grained authorization, guardrails, audit trails, evaluation workflows, cost attribution, and reliable runtime infrastructure. Partner with Security and internal engineering teams, lead incident response and on-call operations, establish connector standards, and mentor engineers building on the platform.

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.

Build and debug LLM agents, integrations, and AI workflows using Python and APIs. Provide technical office hours, mentoring, code reviews, debugging support, and hands-on training. Develop reusable starter kits, frameworks, evaluations, and troubleshooting guides on approved platforms such as Maestro. Diagnose issues involving prompts, retrieval, tool calls, state, permissions, and failures, escalating complex production or security concerns to specialist teams.