Software Engineer, AI/ML

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 RoleYou'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: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 investigateLLM 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 costDetection systems for anomalies in product behavior, performance, and user experience across noisy real-world dataThe 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 definedThe context engineering that lets a model actually understand a customer's codebase: retrieval, indexing, context construction, and the integrations into their stackSystems 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 afterthoughtWhat we're looking forYou've shipped ML or LLM-powered systems into production, where real users depend on the output, not just a notebook or a demoYou move between research and engineering comfortably, and you pick up unfamiliar tech fast. You can go from a new framework or technique on Monday, to a working prototype by Friday, to an eval that tells you whether it's actually better the following weekYou can own something 0→1, beginning to end. You can take an ambiguous AI problem, define the next useful step, and ship without waiting for a fully specified planYou treat evals as a first-class engineering problem. When the system is making decisions for users, intuition isn't a substitute for measurementExperience with agent architectures, tool use, multi-step reasoning, or autonomous workflows in productionNice to haveBackground in code understanding, program analysis, or systems that reason over source codeBuilt retrieval, RAG, or context-construction systems against large or messy dataAnomaly detection, time-series modeling, or learned monitoring systems experienceInternalized understanding of AI/ML systems - a heightened understanding of the inner workings of the systems you'll be buildingStrong signalsAn agentic system you built that other engineers actually chose to use, or that ran in production at meaningful scaleOpen-source work, technical writing, or research with real depth — bonus if it shows where you disagree with the consensusYou've shipped agents or LLM features into production and have informed opinions about what works, what's hype, and what's still brokenYou designed and ran evals that materially changed the trajectory of a product or research directionYou started something from zero, whether a research direction, a product feature, or an internal toolYou dropped into an unfamiliar domain, whether a new modality, a new codebase, or a new sub-field, and shipped real work in it fastHow We WorkWe're in person in New York City. The hardest parts of building Interfere, from system design to architecture tradeoffs to taste calls on the product, happen faster and better at a whiteboard with people physically in the same room.We measure work, not hours. Time at a desk is a poor proxy for whether work is getting done. But there's a lot to do and genuine urgency to being the category winners. Most people who do well here end up putting in serious hours because the problems are interesting and the upside is real.We ship daily, and we ship deliberately. Speed and taste are not in tension here. Every line of code is a choice: we don't let tech debt accumulate because velocity is easier. We write the code we would want to inherit, while still pushing meaningful changes every day.The roadmap is structure, not scaffolding. We plan out the week, so there's structure to what you take on. But the items on that roadmap are whole features and subsystems, each one a project in itself. If you see another problem along the way that needs solving, you own that too. It's your job to make your work into what it needs to be.Compensation and logisticsHealth, dental, and visionVisa sponsorship for exceptional international candidatesWe'll help you move to New YorkInterview processIntro conversation with the founderOnsite/work trial with the team in New YorkReferences and offerHow to applySend us your resume or LinkedIn, plus one piece of work we should look at. We want to see how you think and build. A short note on why Interfere helps, but the work matters more.