At insitro, we are building a different kind of drug company to bring better drugs faster to the patients who can benefit most. Through the power of machine learning (ML) and data at scale, we decode the complexities of biology to unlock transformative new... Backed by SoftBank VF, a16z and GV.
About the role
insitro is a physical AI company dedicated to unlocking causal human biology and accelerating the delivery of better medicines to patients. Our unique Virtual Human™ platform identifies novel, high-impact genetic intervention points, which our TherML™ platform translates into therapeutics—whether small molecules, biologics, or oligos.
What they're looking for
- Tenure: 2–4+ years of experience as a professional software engineer
- Engineering Fundamentals: Working knowledge of AWS or GCP, relational databases, and standard practices like version control and code review
- Domain Experience: Experience with LIMS, lab automation, or another life sciences domain
- Agent/LLM Experience: Experience building with LLMs or agent frameworks — tool and skill design, evaluation, or getting a model to behave reliably against real systems
- Stack Familiarity: Experience with Django, FastAPI, SQLAlchemy, React, TypeScript, PostgreSQL, Docker, or AWS
More about this role
insitro is a physical AI company dedicated to unlocking causal human biology and accelerating the delivery of better medicines to patients. Our unique Virtual Human™ platform identifies novel, high-impact genetic intervention points, which our TherML™ platform translates into therapeutics—whether small molecules, biologics, or oligos. With multiple programs in metabolic disease and neuroscience advancing toward the clinic, and our first IND submission slated for the second half of this year, we are at a pivotal inflection point.
To enable that mission, we need a software layer that ties together the scientific, automation, and machine learning platforms — that's our Lab Platform: a harness for science, built as an agentic workflow system that lets scientists drive the lab through an agent. Our bar is that everything in the lab should be agentically accessible (any action a scientist can take, an agent can take), agentically legible (agents understand what the data means in insitro's context, not just how to fetch it), and humanly verifiable (a scientist can always check what an agent did and why). This is real robotics, workflow automation, and applied agent tooling — and your...
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