AI Engineer — San Francisco Bay Area

A high-growth AI-native startup is hiring an AI Engineer in San Francisco to design, build and ship LLM-powered product features and agentic workflows into production.

This is a build role focused on the applied AI stack: prompts, retrieval, tools, agents, evals and the glue that connects them to a real product. You’ll work close to product and real users, not inside a research lab.

Role: AI Engineer

Salary: $200,000 - $300,000 plus equity

Location: San Francisco, 5 days per week in office

What you’ll be doing:

• Design and ship production LLM features and agents using Python and TypeScript

• Build the applied AI toolkit end to end: tool and function calling, RAG, vector search, structured output, prompt management, context handling

• Work directly with foundation model APIs (Anthropic, OpenAI, Google) and open-source models where it makes sense

• Build evaluation pipelines (Braintrust, LangSmith or custom) so quality is measurable rather than anecdotal

• Integrate agents into existing product surfaces built in React and Next.js

• Own cost, latency and reliability of the AI layer as first-class concerns

• Use AI tooling (Claude, Cursor and similar) across your workflow to compress the build cycle

What you’ll need:

• Strong Python and TypeScript, with real production experience shipping LLM-powered features

• Practical grasp of the applied AI stack: function calling, RAG, vector databases, structured output, prompt engineering, context management

• Comfort with at least one agent framework or SDK, with a view on why the abstractions exist

• Experience building evals and guardrails for LLM output

• Solid API, integration and backend engineering fundamentals

• Fluent in modern AI engineering tools and able to use them to raise output

• Happy working 5 days a week in-person with a small, high-intensity team

Nice to have:

• Prior work on agentic systems in production (not just prototypes)

• Experience with multimodal models (vision, audio, video)

• Exposure to fine-tuning or post-training of foundation models

This role would suit an AI engineer who wants to ship real LLM product into users’ hands, not write another whitepaper.