Software Engineer, Data

Interfere•New York, NY

1d•Onsite

About The Position

Interfere is building a product-quality platform that detects real-time user experience failures and gives teams the context to understand and fix them. As the Software Engineer, Data, you will own the data backbone behind the product. Events, traces, logs, runtime behavior, code, and session data will flow through the systems you build, store, and query. The role focuses on making that data accurate, fast, and affordable at scale. You will work closely with AI/ML and product teams to design pipelines, schemas, and storage systems, and make early architectural decisions that will shape the systems inherited by future engineers. This includes high-throughput ingestion, real-time stream processing, storage and query systems, indexing and retrieval infrastructure for agents and models, evolving schemas and data-quality systems, and the cost, observability, and reliability foundations for Interfere's own data systems. Interfere is a seven-person team based in New York City, has raised $5.1 million, and is already running in production with design partners. The company works in person in New York. It measures work rather than hours, plans work by the week, and ships daily while emphasizing deliberate implementation and avoiding unnecessary technical debt. The role calls for taking ownership of ambiguous problems and putting in serious hours when needed.

Requirements

Experience building and operating production data infrastructure at meaningful scale, with significant throughput, cost pressure, and consequences for failures.
Fluency in distributed-systems tradeoffs, including streaming versus batch, consistency versus latency, full fidelity versus sampling, and storage versus compute.
Ability to select an appropriate technical approach for the problem rather than relying on the latest trend.
Ability to define and ship useful next steps on ambiguous data or infrastructure problems without waiting for a fully specified plan.
Treat infrastructure cost as a core design consideration.
Ability to clearly explain pipeline behavior, failure modes, and tradeoffs to engineers, AI researchers, and product managers.

Nice To Haves

Deep experience with high-throughput streaming or stream-processing systems such as Kafka, Flink, Kinesis, or Materialize.
Background in observability, telemetry, or session-replay data systems.
Experience building vector or hybrid retrieval infrastructure for AI/ML use cases.
Comfort across the full data lifecycle, including ingestion, transformation, storage, query, retention, and deletion.
Fluency in Go, Rust, Python, TypeScript, or another language suited to the throughput requirements.

Responsibilities

Build and operate high-throughput ingestion systems for product events at very large scale.
Build real-time stream-processing systems for detection and diagnosis, balancing correctness and low latency.
Design and build storage and query systems, including choosing suitable data models and technologies as requirements evolve.
Build indexing and retrieval infrastructure that helps agents and models find relevant context.
Develop schema, taxonomy, and data-quality systems that support evolving event shapes and new product surfaces.
Build cost, observability, and reliability capabilities for Interfere's data systems.
Work closely with AI/ML and product teams to design the pipelines, schemas, and storage underlying the product.
Make architectural decisions for data infrastructure that future engineers will build upon.
Take ownership of ambiguous data and infrastructure problems, identify useful next steps, and deliver solutions.