Engineer, Software Engineering

Position Summary

The successful candidate will help design and evolve a biological research platform that turns experimental work into organized, accessible, analysis-ready data. This is a platform and automation role, not a series of isolated programming tasks: the work is owned from design through operation, and the result should preserve sample lineage and experimental context.

Responsibilities

• Learn how experiments are run and how samples and results are tracked, and identify where manual handling slows progress.
• Identify recurring needs, design reusable data models and workflows, and carry them through implementation, testing, and operation.
• Design schemas and interfaces spanning computational screening inputs and outputs through final experimental records, connecting compute and experimental work and enabling powerful querying.
• Build or orchestrate pipelines that parse heterogeneous files and records, harmonize metadata, validate results, and load shared systems, replacing repetitive manual steps with recoverable workflows across laboratory records, cloud storage, databases, and external services.
• Make data available through APIs, visualizations, and AI-enabled interfaces.
• Configure and operate shared cloud infrastructure safely, distinguishing between testing a change and applying it.
• Preserve provenance, units, controls, and QC; write meaningful tests; and catch bad joins or transformations, including in generated code.
• Work directly with experimental scientists on metadata conventions, ambiguous records, and workflows that fit how the lab works, so the team can easily find, compare, and act on in-house data alongside the literature and other advances in the field.

Qualifications

• 3+ Years of experience post M.S., preferably in Computer Science, exposure to biology preferable
• Hands-on programming, effective orchestration of AI coding agents, or both, with the ability to specify architecture and constraints, validate what is built, and take responsibility for the result
• Data modelling skills, including relational models and ORMs, sample and experiment identity, and how modelling choices affect later queries (current stack: PostgreSQL on AWS RDS, SQLAlchemy, and Alembic)
• Python data engineering experience (Pandas, NumPy, and PyArrow are useful)
• Integration and automation experience (current stack: FastAPI, Benchling, REST, and MCP)
• Working knowledge of AWS (S3, RDS, EC2, Secrets Manager, and Bedrock), including credentials and permissions
• Strong judgement on data integrity
• Demonstrated delivery matters more than any particular tool, or than having written every component by hand
• Biological knowledge strongly preferred (genes and proteins, cell types, antibody assays, transcriptomics, proteomics, controls, and replicates), and comfort collaborating with experimental scientists
• Helpful, but not required: Laboratory automation: connecting instruments or control systems to software and AWS. The role may later extend from data handling into experimental automation.
• Helpful, but not required: LLM applications: structured extraction, evidence checks, evaluation, and cost control with Bedrock, Anthropic, or OpenAI.
• Helpful, but not required: Interfaces: React/TypeScript, Vite, and Plotly.
• Helpful, but not required: Familiarity with relational databases