Software Engineer, AI/ML

About 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.

Responsibilities

• Build 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.
• Develop 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.
• Design and implement detection systems for anomalies in product behavior, performance, and user experience across noisy real-world data.
• Construct and maintain evaluation sets, datasets, observability, and feedback loops that move us from "this prompt feels good" to "this system measurably works and is getting better."
• Engineer context engineering that lets a model actually understand a customer's codebase: retrieval, indexing, context construction, and integrations into their stack.
• Build 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.

Requirements

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.

Qualifications

• 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.
• Designed and ran evals that materially changed the trajectory of a product or research direction.
• Started something from zero, whether a research direction, a product feature, or an internal tool.
• Dropped into an unfamiliar domain, whether a new modality, a new codebase, or a new sub-field, and shipped real work in it fast.

Skills

• 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.

Benefits

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.

Pay

Compensation and logistics Health, dental, and vision Visa sponsorship for exceptional international candidates

Schedule

We'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.