AI Engineer Intern

Tanagram's mission is to enable developers to work at the speed of thought. To do that, we're building a tool that captures hard-won lessons buried in codebases, code reviews, incident post-mortems, and Slack chats. We turn those lessons into real-time guardrails that flag or fix risky patterns the moment they reach a pull request — and, eventually, at code generation time — so that engineers can ship faster and avoid disaster.

Imagine an ideal staff engineer. They know the entire codebase and how different systems interact. They follow every PR, so they know what changes are being made and how patterns evolve. They've read all the documentation. They keep up-to-date on Slack conversations. Ultimately, they index all that information in their heads, and deliver impact by showing up everywhere and saying the right things at the right time.

We're building that, but at scale for entire teams and companies. Tanagram is an extension of every team's best staff engineer, available anywhere and anytime.

As an AI Engineer Intern, you'll bridge the gap between AI research and application by using the latest research and models to build our product. Our product, so far, is almost entirely backend. We mostly build in Python, with some Swift for CPU-bound operations.

We’re a small team of generalists and work across multiple domains. We're looking for meticulous, high-agency people who have the skills (or learning ability) to solve problems expediently, and an understanding of the appropriate quality bar given the surrounding business context.

We will generally work in-person in San Francisco (our office is in Mission Bay) or New York.

• Talk directly to users: understand their requirements, ask for their feedback, follow up as needed, and iterate based on what they say.

• Evaluate and build with the best tools in an AI agentic stack: LLM-Evals, Guardrails for AI, CodeAct (agents writing code), memory for agents (like Mem0).

• Build services to ingest data from places like Github, Slack, and Confluence.

• Deploy & use reasoning models like Qwen2.5-7B-Instruct.

• Eagerly identify and implement improvements to our core foundations (e.g. clearing performance bottlenecks for scalability).

• Ensure that systems are efficient, maintainable, and well-monitored.

• Challenging work with the latest advances in LLM techniques.

• Top-of-market compensation.

• Computer/office equipment stipend.

• Food stipend/reimbursements on meals.

• A relatively un-chaotic working environment (we aren't pivoting every week).

• An opportunity to help define our company.

• Hands-on experience building with LLMs, with a strong intuition for what models can do and how to get the best results out of them.

• Well-crafted (i.e. generally reliable; tastefully designed) work examples.

• Bonus points:

  • Experience working on parsers and compilers (e.g. LLVM; Typescript), type systems (e.g. Sorbet), or VSCode or IntelliJ itself.

  • Experience with search and/or indexing technologies.

  • If you've previously worked at a startup, or founded one yourself.

Depending on the relevance and amount of your experience, the salary for this position ranges from $6,900 to $11,000 per month.