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AI Engineer
Digital Waffle
2 hours ago
Full-time
On-site
San Francisco, California, United States
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.
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.
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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.
#J-18808-Ljbffr