Software Engineer, AI/ML

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

• Open-source work, technical writing, or research with real depth — bonus if it shows where you disagree with the consensus

• You've shipped agents or LLM features into production and have informed opinions about what works, what's hype, and what's still broken

• You designed and ran evals that materially changed the trajectory of a product or research direction

• You started something from zero, whether a research direction, a product feature, or an internal tool

• You dropped into an unfamiliar domain, whether a new modality, a new codebase, or a new sub-field, and shipped real work in it fast

How We Work

• 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.

• 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 logistics

• Health, dental, and vision

• Visa sponsorship for exceptional international candidates

• We'll help you move to New York

Interview process

1. Intro conversation with the founder

2. Onsite/work trial with the team in New York

3. References and offer

How to apply

Send 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.

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.