Senior Software Engineer Jobs

About The Position

Requirements

• Significant professional experience building production data systems and pipelines (scope and judgment are prioritized over years of experience).
• 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). Experience with Airflow or Dagster is transferable.
• 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 integrating an LLM or agent component into a production data path, including the evaluation loop, gold set, and cost per record.
• Daily use of AI coding tools, coupled with a healthy skepticism regarding their output on production data correctness.
• A track record of owning work through to a running, validated system, including addressing issues like malformed datasets, writing backfills, and debugging failed processes.
• Comfort with messy scientific formats and toolchains (mzML, RDKit, ProteoWizard or similar). Strong engineering depth is required.
• A passion for contributing to an early-stage startup, valuing autonomy, eagerness to learn, and enthusiasm for solving novel scientific challenges over rigid processes and egos.

Responsibilities

• Build and Scale Data Contracts: Own the pipelines and systems that other teams consume. Design and implement systems that effectively scale to multiple petabytes of data.
• Serving and Cost at Scale: Build and scale systems for acquiring, storing, and serving data efficiently and affordably as it grows, focusing on data layout, featurization throughput, Kubernetes-native orchestration, and cost management.
• Labels and Enrichment: Transform raw data into usable datasets with consistent schemas, trustworthy metadata, and documented definitions. Scale scientific labels from studies down to their spectra and underlying features.
• Quality Gates: Automate quality checks to enable increasing capability without regression, promoting only on successful completion.
• Interfaces People Use: Own the interfaces that AI, chemistry, product, and agents interact with, from the SDK for building datasets to the tools exposing platform capabilities.
• Operations and Data Rights: Ensure the effective operation of our data needs, meeting designed service level agreements while providing high-quality, provenance, and secure data processing in line with customer needs.

Benefits

• Competitive base salary
• Stock options
• Health & dental insurance
• Vision insurance
• Long- and short-term disability insurance
• Life insurance
• 401k with company match
• Flexible work policy
• Unlimited time away policy
• Commuter benefits
• Parking
• Regular team meals and outings
• Company support for continued education/coursework
• Company support for conference participation