Senior / Staff Full Stack Software Engineer, Insitro

• You have 5+ years of experience as a professional software engineer with 3+ years of full stack web development experience
• You're eager to ship work, wherever it is on the stack, that makes a difference to scientists and ultimately patients
• You have a track record of turning ideas into maintainable and user-centric software products
• You feel comfortable reasoning about the tradeoffs between quality and speed when building in a startup environment
• You're familiar with the usual SWE things: AWS (or GCP/Azure), relational databases, writing design docs, version control, doing code reviews, perfecting our slackmoji game

Desirable

• In this role, you will work primarily with heterogenous, multimodal clinical datasets, and build tools to help characterize cellular and patient state, predict the effect of clinical interventions, and design more successful clinical trials
• To achieve this, you'll partner directly with machine learning scientists, biologists, engineers, and clinicians to design and build a cohesive platform for data ingestion, transformation, and exploration
• Ship stuff that makes our scientists say "this is amazing, thank you so much!"
• Collaborate cross-functionally with folks from our machine learning, automation, and biology groups
• Directly shape our roadmap for empowering scientists
• Shape our early-ish engineering culture with your ideas and experience
• All the normal SWE stuff: write code, write and review design docs, talk to collaborators, and do code reviews
• Ultimately you'll move the needle in a meaningful way for insitro and the field of medicine
• Architecting components of our clinical data platform, spanning data ingestion, warehousing, and discovery
• Building a data portal inspired by tools like HuBMAP and GDC, to make insitro's clinical data accessible to technical and nontechnical collaborators alike
• Designing intuitive interfaces for visualizing and annotating radiology, digital pathology, and other high-content data and the insights that are derived from them
• Embedding deeply with ML scientists, and helping them build multimodal models that map learned phenotypes to clinical outcomes