Engineer, Software Engineering
Flagship Pioneering · Cambridge, MA USA
About this role
**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. --- ## Position Summary FL103 is seeking an **Engineer, Software Development** to help design and evolve our biological research platform. 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/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. --- ## Qualifications - **3+ years** of experience post M.S., preferably in Computer Science (biology exposure is preferable). - Hands-on programming and/or effective orchestration of AI coding agents—able to specify architecture/constraints, validate what is built, and take responsibility for outcomes. - Data modeling skills (relational models and ORMs), including sample/experiment identity and how modeling choices affect later queries. - Current stack: **PostgreSQL on AWS RDS**, **SQLAlchemy**, **Alembic**. - Python data engineering experience (e.g., **Pandas**, **NumPy**, **PyArrow**). - Integration and automation experience (current stack: **FastAPI**, **Benchling**, **REST**, and **MCP**). - Working knowledge of **AWS** (S3, RDS, EC2, Secrets Manager, Bedrock), including credentials and permissions. - Strong judgment on data integrity; demonstrated delivery matters more than any particular tool. - Biological knowledge strongly preferred (genes/proteins, cell types, antibody assays, transcriptomics, proteomics, controls, replicates) and comfort collaborating with experimental scientists. --- ## Helpful, but not required - Laboratory automation (connecting instruments/control systems to software and AWS). The role may later extend into experimental automation. - LLM applications: structured extraction, evidence checks, evaluation,
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