Roche's AI for Drug Discovery group is building a platform called Lab-in-the-Loop that couples generative ML with automated wet-lab experimentation to continuously design, test, and learn from new therapeutic molecules. The goal is to accelerate R&D and deliver more innovative medicines by leveraging AI and data.
Requirements
- 3+ years building and operating complex, full‑stack applications (you’ve owned systems in production).
- Proven front‑end expertise with modern frameworks (e.g., React, Angular) and a track record of delivering intuitive, user‑centered interfaces.
- Proficiency with Python and SQL; comfortable designing data models and pipelines.
- Strong system design skills: domain modeling, event‑driven architecture, concurrency/failure modes, performance profiling.
- Familiarity with machine learning concepts, model training, serving, and evaluation.
- Practical hands-on experience with PyTorch in a production environment.
Responsibilities
- Build and enhance our full-stack platform, crafting intuitive front-end applications and engineering the reliable backend services that orchestrate the entire discovery cycle.
- Build the critical integrations that close the loop with the wet lab, connecting our platform with robotics, assay pipelines, and instrumentation data.
- Engineer the systems that bring our AI to life by integrating novel generative models and building the infrastructure for their evaluation and deployment.
- Shape the strategic roadmap for Lab‑in‑the‑Loop, influencing its features and technical architecture.
- Partner directly with ML scientists and experimentalists, translating complex scientific workflows into elegant and effective product features.
Other
- Relocation benefits are available for this job posting.
- Genentech is an equal opportunity employer.
- If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
- Curiosity about the biology of disease and eagerness to contribute to scientific and computational efforts.
