Data/ML Infrastructure Engineer

You have strong software engineering fundamentals and have built production systems where reliability, cost, and performance matter. You can reason clearly about distributed systems tradeoffs, and you have experience designing data-intensive infrastructure that other engineers depend on.

You are comfortable working across data platform and ML platform concerns, and you understand how tightly coupled they become in production. You care about reproducibility, debuggability, and developer experience because you have seen how quickly they become bottlenecks.

You work effectively across research and product teams. You can translate ambiguous needs into clear interfaces and systems, and you can drive work from design through production while maintaining a high quality bar.

A few things we expect in this role:

Meaningful experience building production data infrastructure, ML infrastructure, or distributed systems

Strong programming skills in Python and SQL, with the judgment to choose the right abstractions and interfaces for production systems

Experience building and operating systems on AWS

Familiarity with modern infrastructure and platform tooling, including Kubernetes, Docker, and Terraform

Experience working with production storage and serving systems such as Postgres and Redis

Familiarity with data and ML workflow tooling such as Metaflow

Strong instincts for observability, testing, and operational excellence

Experience supporting ML training, evaluation, batch inference, or model deployment in production

Familiarity with modern large-scale data patterns and tooling, including streaming, backfills, partitioning strategy, and schema evolution

Experience building internal platform primitives such as data versioning and lineage, dataset curation, experiment tracking, or tooling for reproducible workflows

Exposure to perception, multimodal, or geospatial systems, especially where data originates from real sensors and is used in real products

You are a strong engineer who likes building reliable systems that other teams can trust. You care about infrastructure quality, operational rigor, and clear interfaces. You are energized by working close to the data, close to the models, and close to the product.