Tanagrams mission is to accelerate agentic coding. Were 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.Were building a small team of exceptional engineers who are excited about the future of agentic coding and think deeply about codebases from first principles. Were 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.<h1>About This Role:</h1>As an ML Product Engineer, youll leverage the latest ML tools and techniques to enable product functionality, including:<ul><li>Analyzing enterprise-scale codebases for implicit dependencies.</li><li>Extracting engineering patterns from various data sources</li><li>Implementing and iterating on similarity searches across codebase patterns, taking into account the inherent structure and dependencies in codebases.</li></ul>This role is exploratory — we have a good sense of what success looks like, but we dont 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.<h1>Responsibilities:</h1><ul><li>Research &amp; apply ML algorithms: clustering techniques, similarity search, entity recognition, etc.</li><li>Build knowledge graphs from multiple data sources.</li><li>Augment user inputs with additional context through traditional ML algoritms and/or reasoning models like Opus 4 or Qwen2.5-7B-Instruct.</li><li>Build production-grade features around these algorithms/models, ship them to users, and respond quickly to user feedback (e.g. fixing bugs within hours).</li><li>Share and promote your work publicly (e.g. on Twitter, LinkedIn, Reddit, etc).</li><li>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.</li></ul><h1>What We Offer:</h1><ul><li>Challenging work on enterprise-scale codebases and datasets.</li><li>Unlimited token usage for development using <a target="_blank" href="http://ampcode.com/">Amp</a>.</li><li>Top-of-market compensation (and a long runway).</li><li>Employee-friendly equity terms (low FMV, early exercise, extended exercise).</li><li>Your choice of Macbook Pro + computer/office equipment stipend.</li><li>Health, dental, and vision insurance.</li><li>Unlimited PTO.</li><li>A relatively un-chaotic working environment (we arent pivoting every week).</li><li>An opportunity to lead and define our company.</li></ul><h1>Qualifications:</h1><ul><li>Experience with ML/NLP techniques on production projects.</li><li>Strong generalist engineer — you’re comfortable across the stack, include MLOps, SQL, Python, and building against APIs.</li><li>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.</li><li>Bonus points:<ul><li>Experience building knowledge graphs and working with graph databases.</li><li>If youve previously worked at a startup, or founded one yourself.</li></ul></li></ul><h1>Compensation:</h1>Depending on the relevance and amount of your experience:<ul><li>Salary for this position ranges from $160,000 to $205,000 USD</li><li>Equity ranges from 0.5% to 1.5%.</li></ul>If we move forward with an offer, you will have a choice between more cash or more equity.

WindBorne Systems

ML Product Engineer (Junior)

WindBorne Systems