Software Engineer, AI/ML

About the role

Interfere turns a product's invisible failures into shared problems the whole team can see and fix. Every app has places where users get confused, blocked, or forced to abandon a flow, but most of those moments never make it into a support ticket. We detect those failures in real-time and provide every person responsible for the fix with the context they need. When Interfere flags a broken checkout flow, the PM uses our data to prioritize the issue, the designer sees where the experience broke down, and the engineer pulls the trace underneath. We're building the operating system for product quality, so teams can move from scattered symptoms to a shared understanding of what's actually going wrong. We're a seven-person team in New York, with $5.1M raised from Y Combinator, Vercel Ventures, Hummingbird, Designer Fund, and others. Interfere is already running in production with design partners, which means the work you ship will immediately help real teams find and fix the failures costing them users today. The category is still being defined, but the product to fill this gap is inevitable, and the company that gets there first will own how the next decade of teams ship software. We're looking for the people who will move at the speed that demands.

The Role

You'll own the intelligence layer that makes Interfere work. The product is only as good as the systems that decide what's broken, what caused it, and what to do about it — and those systems live inside the agents, evals, and inference pipelines you'll build. You'll work where research meets production, shipping things that run at scale against real codebases and runtime data. Concretely, this looks like building:

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Agents that reason about codebases, traces, logs, and runtime behavior to find and explain bugs before users hit them, including the sandboxes they run in and the browsers and web research they use to investigate

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LLM pipelines for triage, root-cause analysis, and automated fix proposals, with the model routing that sends each job to the right model at the right cost

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Detection systems for anomalies in product behavior, performance, and user experience across noisy real-world data

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The evals, datasets, observability, and feedback loops that move us from "this prompt feels good" to "this system measurably works and is getting better," architecting the standards for the agentic parts of a product that is still being defined

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The context engineering that lets a model actually understand a customer's codebase: retrieval, indexing, context construction, and the integrations into their stack

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Systems that get sharper over time, turning every bug found and fixed into signal that improves the next detection, so continual learning is a product advantage rather than an afterthought

What we're looking for

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You've shipped ML or LLM-powered systems into production, where real users depend on the output, not just a notebook or a demo