AI Engineer

Digital Waffle; San Francisco (CA); On-site; USD 180,000 - 320,000; Full time

Job summary

Digital Waffle in San Francisco is hiring an AI Engineer to design, build and ship LLM-powered features and agentic workflows into production. You’ll work closely with product and users, not just in a lab, delivering end-to-end AI capabilities.

The role focuses on the applied AI stack: prompts, retrieval, tools, agents, evals and the glue that connects them to the product. Expect hands-on work with models via APIs and tooling.

Qualifications

• Experience shipping LLM-powered features in production.
• Strong Python and TypeScript, with production experience shipping LLM features.
• Familiarity with function calling, RAG, vector databases and structured output.
• Experience building evals and guardrails for LLM output.

Responsibilities

• 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 with foundation model APIs (Anthropic, OpenAI, Google) and open-source models where appropriate.
• Build evaluation pipelines so quality is measurable, not anecdotal.
• Integrate agents into product surfaces built in React and Next.js.
• Own cost, latency and reliability of the AI layer as a first-class concern.

Skills

Python

TypeScript

LLM features

Production experience

APIs

Tools

OpenAI API

Anthropic API

LangSmith

vector databases

Job description

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.