Machine Learning Engineer

Role overview

This position is centered on productionizing machine learning work, moving models out of research notebooks and into the live products that real users interact with every day. The engineer will own the path from prototype to reliable, scalable service inside consumer-facing applications.

Responsibilities

- Convert experimental notebook code into production-grade model services - Build and maintain the infrastructure required to serve models at scale inside everyday products - Collaborate with research and product engineering teams to align deployments with feature requirements - Monitor deployed models for reliability, latency, and quality, and iterate on improvements - Apply software engineering discipline to ML code so it is testable, maintainable, and observable

Requirements

- Hands-on experience taking machine learning models from prototype through to production deployment - Strong software engineering skills that extend well beyond notebook experimentation - Familiarity with the tooling and infrastructure used to train, serve, and scale ML systems - Solid understanding of common ML frameworks and deployment best practices - Comfort working at the intersection of research and product engineering

Nice to have

- Experience with model monitoring, evaluation pipelines, or MLOps tooling - Background contributing to consumer-facing products where models ship at scale