What does a Software Engineer do?
In life sciences, software can sit between an instrument and a scientist, or between a computational model and the team trying to use it. A Software Engineer turns fragile research workflows into systems that other people can operate, test and trust. Biology knowledge may help, but sound software fundamentals remain central.
The main tasks
- Translate user needs into reliable software
- Write and test application components
- Maintain observability, security and changes
What the work looks like
A typical week
A week may involve interviewing researchers, defining data models, writing Python services or application interfaces, reviewing code and fixing failed scientific workflows. The engineer might connect an instrument to a data platform, build a run-monitoring tool or make a laboratory process usable without a command line. Production support often matters as much as new features.
Every employer, team and project is a little different.
What the work produces
Applications, services, documentation and production-ready code.
The judgement calls
The engineer chooses what belongs in a reusable platform and what should stay a bespoke workflow. They must trade development speed against data integrity, maintainability, access control and observability. Scientists may tolerate one-off scripts in exploration but depend on repeatable behavior when a workflow becomes operational.
A screening run finishes successfully on the robot, but sample identifiers fail to attach to the measured values. The engineer traces the event through instrument output, ingestion logic and database constraints, then adds tests so the problem cannot silently recur.
What you need to know
Strong roles mention testing, APIs, SQL, Python or another major language, cloud services, version control, CI, containers, data modeling and debugging. Scientific positions add instrument integration, workflow orchestration, provenance, sample metadata or cheminformatics formats. React, Postgres, Prefect and AWS are examples seen in specific advertisements, not universal requirements.
Knowledge
- Python for Scientific Computing Core
- SQL Important
- Research Reproducibility Useful
Skills
- Build reproducible Python analyses Core
- Write traceable SQL joins Important
- Design controlled experiments Useful
- Core
- used most weeks
- Important
- needed at some point
- Useful
- helps, often learned on the job
How the role tends to work in general, never a requirement any employer has set.
Who you work with, and where the job ends
Works with scientists, product or design colleagues, platform engineers and instrument or data system owners.
Bioinformatics Engineers usually own analysis workflows with biological meaning. Software Engineers often own broader applications and operational systems, although scientific software positions overlap both. Do not create separate canonical roles just for Python, Ruby or a cloud provider.
Often confused with Data Engineer
How people get into it
Software engineering practice. Biomedical domain knowledge depends on product.
Where this profile comes from
Written by TheBench.Bio for the field in general, and reviewed on October 23, 2026. It draws on what employers ask for in real job adverts:
- Iambic Therapeutics, Software Engineer, Scientific Computing
- OpenAI, Software Engineer, Life Sciences
- Uncountable, Software Engineer, Implementations
A profile describes the work most people in the role share. One employer's version can be narrower or broader.