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.
About This Role: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.
Responsibilities: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.
At least a few years of IC experience — this role generally maps to a "senior" engineer level.
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 $200,000 to $275,000 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.
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What We Do
We’re specialists in pre-seed and seed. The startups we back go far. Best-in-class founders do not come around every day. When they do, we jump at the opportunity to work together. Our approach is to work with just a small number of best-in-class founders so we can dig in and go deep.
