# Software Engineer, AI/ML at Interfere > Onsite · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Ainterfere%3A2b33c914-e6f7-44a4-9859-e996da7f4e87 (read 2026-11-04). ## Facts (observed) - Company: [Interfere](https://alderhq.app/companies/interfere) - Location text: New York City - Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York City") - Posted: 2026-07-27 (100 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/interfere/2b33c914-e6f7-44a4-9859-e996da7f4e87 - Skills listed (inferred): LLMs ## Pay and sponsorship - Posted pay: $160k–$250k per year (from the listing's pay field) ## Description (the employer's text) 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: 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 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 Detection systems for anomalies in product behavior, performance, and user experience across noisy real-world data 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 The context engineering that lets a model actually understand a customer's codebase: retrieval, indexing, context construction, and the integrations into their stack 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 You've shipped ML or LLM-powered systems into production, where real users depend on the output, not just a notebook or a demo You 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 week You 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 plan You treat evals as a first-class engineering problem. When the system is making decisions for users, intuition isn't a substitute for measurement Experience with agent architectures, tool use, multi-step reasoning, or autonomous workflows in production ### Nice to have Background in code understanding, program analysis, or systems that reason over source code Built retrieval, RAG, or context-construction systems against large or messy data Anomaly detection, time-series modeling, or learned monitoring systems experience Internalized understanding of AI/ML systems - a heightened understanding of the inner workings of the systems you'll be building ### Strong signals An agentic system you built that other engineers actually chose to use, or that ran in production at meaningful scale --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Ainterfere%3A2b33c914-e6f7-44a4-9859-e996da7f4e87 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.