What if… you could help build a research platform that turns experimental biology into organized, accessible, analysis-ready data?
Experimental work generates data across many files, records, and systems, and manual handling of samples and results slows progress. A reliable computational platform can preserve sample lineage and experimental context and make data available through APIs, visualizations, and AI-enabled interfaces, so scientists can easily find, compare, and act on in-house data alongside the literature and other advances in the field.
FL103 is seeking an Engineer, Software Development to help design and evolve our biological research platform. The successful candidate will work with the computational modeling and experimental teams to learn how computational outputs become experimental inputs, how results return, and where manual handling slows progress. They will then move as much of that handling as practical onto reliable computational workflows that are transparent and easy to query.
FL103 was founded by Flagship Pioneering, an innovation enterprise dedicated to originating and developing companies that invent breakthrough technologies to transform health care, agriculture, and sustainability. Since Flagship’s founding in 2000, the firm has originated and fostered more than 100 scientific ventures, including Moderna Therapeutics, Generate Biomedicines, and Indigo Agriculture.
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…