Engineer, Software Engineering

flagshippioneeringinc; Cambridge; On-site; GBP 70,000 - 100,000; Full time

Job summary

Flagship Pioneering's FL103 is seeking an Engineer, Software Development to help design and evolve a biological research platform that turns experiments into organized, accessible, analysis-ready data. This platform- and automation-focused role spans design through operation, preserving provenance and context.

You will collaborate with experimental scientists, build reusable data models, pipelines, and interfaces, and operate shared cloud infrastructure with emphasis on data integrity and test

Qualifications

• MS or higher in CS or related field.
• Hands-on programming and orchestration of AI coding agents.
• Data modelling with relational models and ORMs (PostgreSQL, SQLAlchemy).
• Proficiency in Python data engineering (Pandas, NumPy, PyArrow).
• Experience with AWS services and API-first design.
• Experience integrating and automating with modern stacks like FastAPI, REST.

Responsibilities

• Learn how experiments are run and how samples and results are tracked.
• Design reusable data models and workflows, implement and operate them.
• Design schemas and interfaces spanning computational and experimental data.
• Build pipelines to parse files, harmonize metadata, and load systems.
• Make data available through APIs, visualizations, and AI interfaces.
• Configure and operate shared cloud infra safely and test changes.
• Preserve provenance, units, controls, QC and ensure data integrity.

Skills

Python programming

Data modelling

AWS knowledge

SQL/ORM experience

API design

Test-driven development

Education

MS or higher in CS or related field

Tools

PostgreSQL on AWS RDS

SQLAlchemy

Alembic

Pandas/NumPy/PyArrow

FastAPI

Benchling

REST

Job description

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 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.
• LLM applications: structured extraction, evidence checks, evaluation, and cost control with Bedrock, Anthropic, or OpenAI.
• Interfaces: React/TypeScript, Vite, and Plotly.
• Familiarity with relational databases

Flagship Pioneering is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship La