ML Product Engineer (Junior)

Tanagram's mission is to accelerate agentic coding. We're starting by 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 teams of people and agents can ship faster and avoid disaster.

We're building a small team of exceptional engineers who are excited about the future of agentic coding and think deeply about codebases from first principles. We're looking for meticulous, high-agency people who have good judgment around what problems to solve, the skills to build a great product around it, and the hunger to iterate towards better versions.

As an ML Product Engineer, you'll leverage the latest ML tools and techniques to enable product functionality, including:

• Analyzing enterprise-scale codebases for implicit dependencies.

• Extracting engineering patterns from various data sources

• Implementing and iterating on similarity searches across codebase patterns, taking into account the inherent structure and dependencies in codebases.

This role is exploratory — we have a good sense of what success looks like, but we don't yet know how to get there. You should have a good intuition for the right tools to use, and how to configure, combine, and tweak them to deliver the best results for our users.

We will generally work in-person in San Francisco (our office is in Mission Bay), but are open to remote for the right candidate.

• Research & apply ML algorithms: clustering techniques, similarity search, entity recognition, etc.

• Build knowledge graphs from multiple data sources.

• Augment user inputs with additional context through traditional ML algoritms and/or reasoning models like Opus 4 or Qwen2.5-7B-Instruct.

• Build production-grade features around these algorithms/models, ship them to users, and respond quickly to user feedback (e.g. fixing bugs within hours).

• Share and promote your work publicly (e.g. on Twitter, LinkedIn, Reddit, etc).

• Shape our product roadmap by influencing the sequencing of what we want to build, and/or by talking to potential users and proposing new projects.

• Challenging work on enterprise-scale codebases and datasets.

• Unlimited token usage for development using Amp.

• Top-of-market compensation (and a long runway).

• Employee-friendly equity terms (low FMV, early exercise, extended exercise).

• Your choice of Macbook Pro + computer/office equipment stipend.

• Health, dental, and vision insurance.

• Unlimited PTO.

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

• An opportunity to lead and define our company.

• Experience with ML/NLP techniques on production projects.

• Strong generalist engineer — you’re comfortable across the stack, include MLOps, SQL, Python, and building against APIs.

• Self-direction and output-oriented: you repeatedly, independently seek out the most valuable thing you could be doing, to achieve scalable results, quickly. You bias towards action and iteration, not just perfecting models in notebooks.

• Bonus points:

  • Experience building knowledge graphs and working with graph databases.

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

Depending on the relevance and amount of your experience:

• Salary for this position ranges from $160,000 to $205,000 USD

• Equity ranges from 0.5% to 1.5%.

If we move forward with an offer, you will have a choice between more cash or more equity.