Full Stack Software Engineer, Lab Platform (LIMS)

3 нед. назад

СШАSeniorOnsite

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Develop software across the full stack for a lab platform integrating scientific, automation, and machine learning systems at a biotech company.

О компании

is a drug discovery and development company using machine learning (ML) and data at scale to decode biology for transformative medicines. At the core of ’s approach is the convergence of in-house generated multi-modal cellular data and high-content phenotypic human cohort data. We rely on these data to develop ML-driven, predictive disease models that uncover underlying biologic state and elucidate critical drivers of disease. These powerful models rely on extensive biological and computational infrastructure and allow to advance novel targets and patient biomarkers, design therapeutics and inform clinical strategy. is advancing a wholly owned and partnered pipeline of insights and therapeutics in neuroscience and metabolism. Since launching in 2018, has raised over $700 million from top tech, biotech and crossover investors, and from collaborations with pharmaceutical partners. For more information on , please visit.

Обязанности

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 '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 users are in the building with you.
You'll work across the stack — front end, backend, and the integrations that reach into instruments, Benchling, and agent runtimes — partnering closely with our machine learning, automation, and biology teams. Based in South San Francisco, this role reports directly to Senior Manager, Software Engineering and offers an in-person hybrid schedule of three days per week. We'll bring you up to speed in the domain of drug development and back your ideas with real trust and mentorship along the way.

Требования

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

Будет плюсом

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

Условия

Our target starting salary for successful US-based applicants for this role is $111,000 - $140,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.
This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.
In addition, also provides our employees:
401(k) plan with employer matching for contributions
Excellent medical, dental, and vision coverage as well as mental health and well-being support
Open, flexible vacation policy
Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc)
Quarterly budget for books and online courses for self-development
New hire stipend for home office setup
Monthly cell phone & internet stipend
Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid
Access to a free commuter bus network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area
We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams–grounded in a wide range of expertise and life experiences–and work even harder to ensure those teams thrive in inclusive, growth-oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process.
#LI-Hybrid

Другое

Ship Across the Stack: Build the React and TypeScript frontends scientists use every day, the Python services behind them, and integrations that reach instruments, Benchling, and agent runtimes
Own It End to End: Take a feature from the first conversation with a scientist through the data model and UI to the alert that fires when it breaks
Do the Normal SWE Things: Write code, review design docs, talk to users, and do code reviews that actually make the codebase better
Chase User Appreciation: Ship things that make scientists say "OMG, this is amazing, thank you so much."
Figure Out What's Actually Useful: Test agentic tooling ideas through prototypes, real users, and short iterative loops rather than betting on theory
Move the Needle: Contribute work that meaningfully advances 's mission and the pace of drug development
Go Watch the Work: Spend time in the lab observing the workflow before you change it
Partner Broadly: Collaborate closely with machine learning, automation, and biology teams to build tools people actually use
Keep Humans in the Loop: Design for provenance and verifiability, so a scientist can always check what an agent did and why
Product Instinct: You think like a product person — you want to know who the user is, what they're actually trying to do, and you have opinions about what to build
Curious About the Science: You don't need a biology background, but you want to learn the domain rather than treat it as someone else's problem
Comfortable in the Gray Area: You can reason clearly about the tradeoffs between quality and speed
Fits the Team: You're up for writing design docs, having opinions in code review, and yes, goofing around on Slack.