Software Engineer, Data

What we're looking for

• You've built and operated production data infrastructure at meaningful scale, with real throughput, real cost pressure, and real consequences when it breaks

• You're fluent in distributed-systems tradeoffs: streaming vs batch, consistency vs latency, full fidelity vs sampling, storage vs compute. You pick the right answer for the situation rather than the one you read most recently

• You take an ambiguous data or infrastructure problem, define the next useful step, and ship without waiting for a fully specified plan

• You treat cost as a feature. A system that works at 1x and burns the company at 100x isn't finished

• You can explain pipeline behavior, failure modes, and tradeoffs clearly enough that engineers, AI researchers, and PMs can make the right call quickly

Nice to have

• Deep experience with high-throughput streaming or stream-processing systems (Kafka, Flink, Kinesis, Materialize)

• Background in observability, telemetry, or session-replay data systems

• Built vector or hybrid retrieval infrastructure for AI/ML use cases

• Comfort across the full data lifecycle: ingestion, transformation, storage, query, retention, deletion

• Fluent in Go, Rust, Python, TypeScript, or whichever tool the throughput actually demands. We hire for how you reason about data systems, not for a specific language

Strong signals

• A data system you built that other engineers relied on, and that survived contact with real load

• Open-source work, technical writing, or talks where the tradeoffs and the reasoning behind them are visible

• You've replaced a vendor system with one you built, or replaced something you built with a vendor, and have strong opinions about when each is right

• You've cut a meaningful infrastructure bill in half, or doubled throughput on the same bill, without losing correctness

• You picked up an unfamiliar data store, query engine, or infrastructure pattern quickly and shipped something good with it

• You started something from zero, a pipeline, a platform, an internal tool, that people kept using after you left

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.