Software Engineer, Data

About the role

You'll own the data backbone of Interfere. Every signal the product reasons about (events, traces, logs, runtime behavior, code, session data) flows through systems you'll build, store, and query.

The product's intelligence is only as good as the data underneath it, and that data is only useful if it's accurate, fast, and affordable at scale. That's your job.

Responsibilities

• Build high-throughput ingestion systems that handle billions of product events without dropping data, slowing down, or melting the infra bill
• Design and implement real-time stream processing for detection and diagnosis, ensuring the work is correct and low-latency
• Develop storage and query systems (ClickHouse; columnar, time-series, vector, etc.) that stay fast as customer data grows by orders of magnitude
• Create indexing and retrieval infrastructure that lets agents and models find the right context at the moment they need it
• Design schema, taxonomy, and data-quality systems that hold up as event shapes evolve and new product surfaces appear
• Build the cost, observability, and reliability layer for our own data systems, ensuring observability for our customers starts with observability of ourselves

Requirements

• Fluent in distributed-systems tradeoffs: streaming vs batch, consistency vs latency, full fidelity vs sampling, storage vs compute
• Take an ambiguous data or infrastructure problem, define the next useful step, and ship without waiting for a fully specified plan
• Treat cost as a feature. A system that works at 1x and burns the company at 100x isn't finished

Qualifications

• 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 Go, Rust, Python, TypeScript, or whichever tool the throughput actually demands
• You can explain pipeline behavior, failure modes, and tradeoffs clearly enough that engineers, AI researchers, and PMs can make the right call quickly

Skills

• 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
• Fluent in Go, Rust, Python, TypeScript, or whichever tool the throughput actually demands
• Treated cost as a feature

Benefits

• 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
• We ship daily, and we ship deliberately. Speed and taste are not in tension here
• We write the code we would want to inherit, while still pushing meaningful changes every day

Pay

$150k - $200k base salary, depending on experience

Schedule

Full-time, 40 hours per week