Senior Software Engineer, Data Platform

Significant professional experience building production data systems and pipelines. We level on scope and judgment rather than years.

Proficient in Python and SQL for large-scale data processing.

Proficient in Kubernetes-native batch orchestration and modern data lake technologies (Argo Workflows, Metaflow, EKS, Glue, Athena, Apache Iceberg, Parquet, DuckDB, Terraform). Airflow or Dagster experience transfers fine.

Demonstrated experience designing stable identifiers for a large, changing corpus, and building validation that gates a publish rather than reporting on it after the fact.

Experience putting an LLM or agent component into a production data path, including the eval loop, the gold set, and cost per record.

Daily use of AI coding tools, paired with healthy skepticism about their output on questions of production data correctness.

A track record of owning work through to a running, validated system, including the unglamorous parts: fixing the malformed dataset, writing the backfill, debugging last night's bad publish.

Comfort with messy scientific formats and toolchains (mzML, RDKit, ProteoWizard or similar). Engineering depth is the requirement.

A passion for contributing to an early-stage startup where autonomy, eagerness to learn, and enthusiasm for solving novel scientific challenges prevail over rigid processes and egos.

Given the cross-disciplinary and innovative nature of our work, effective collaboration and communication are critical to our progress. We operate in a flexible hybrid model that accommodates both fully remote team members and those who work full-time from our Somerville, MA office. While some positions may require regular in-person presence for hands-on work or local collaboration, many roles can be performed remotely with team members distributed across various locations.