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 ML Product Engineer, you'll leverage the latest ML tools and techniques to enable product functionality, including:
Analyzing enterprise-scale codebases for implicit dependencies.
Implementing recommendation systems for finding code that's similar to a given pattern.
Extracting patterns from code reviews and documentation.
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’re a small team of generalists and work across multiple domains. We're looking for meticulous, high-agency people who have good judgment around what problems to solve, the skills (or learning ability) to solve it 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), 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.
Use reasoning models like Qwen2.5-7B-Instruct to refine queries based on existing knowledge.
Help build with the best tools in an AI stack: LLM-Evals, Guardrails for AI, CodeAct (agents writing code to fulfill goals), memory for agents (like Mem0).
Ensure that systems are efficient, maintainable, and well-monitored.
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.
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.
Food stipend/reimbursements on meals.
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 working with high-volume data in vector databases.
Experience with ML/NLP techniques on production projects.
At least a few years of IC experience, balancing moving fast with scalable implementations. This role generally maps to a "senior" engineer level, although we're somewhat flexible (and will adjust compensation accordingly).
Self-direction and output-oriented: you repeatedly, independently seek out the most valuable thing you could be doing, to achieve scalable results, quickly. You do so even when requirements and priorities may be changing rapidly.
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 $210,000 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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