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Insitro Jobs (NOW HIRING)

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN. We ...

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN. We ...

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Insitro information

What is the difference between Insitro vs Data Scientist?

AspectInsitroData Scientist
Required CredentialsBackground in biology, machine learning, or related fields; advanced degrees often preferredDegree in computer science, statistics, or related fields; often requires experience with data analysis
Work EnvironmentResearch-focused, interdisciplinary teams in biotech or pharmaceutical companiesVaried environments including tech firms, finance, healthcare, with data analysis focus
Industry UsagePrimarily in biotech, drug discovery, and pharmaceutical researchAcross multiple industries including tech, finance, healthcare, and consulting

Insitro professionals typically work in biotech and pharmaceutical settings, combining biology and machine learning, whereas Data Scientists work across diverse industries analyzing data to inform business decisions. While both roles require strong analytical skills, Insitro emphasizes biological knowledge alongside data science expertise.

More about Insitro jobs

What cities are hiring for Insitro jobs?

Cities with the most Insitro job openings:

What states have the most Insitro jobs?

States with the most job openings for Insitro jobs include:

Infographic showing various Insitro job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% Physical job distribution.

Member of Technical Staff -- Product Engineering

San Francisco, CA โ€ข On-site

Other

Re-posted 22 days ago


Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

We look for software engineers who are excited to tackle unsolved problems. As our models reach the real world, predictions must arrive reliably, on hard deadlines, in whatever environment our customers operate. Your mission is to own the path from trained model to customer value โ€” the production systems that serve predictions, the surfaces customers touch, and the demos that turn frontier research into a product, making every deployment easier than the one before it.

Responsibilities
  • Build and operate the production systems that deliver model predictions to customers around hard real-time deadlines โ€” owning reliability end to end, from cost efficiency to monitoring, alerting, and incident response
  • Design and build the full product surface: backend APIs and data delivery, integration patterns, and frontend dashboards and visualizations that make predictions actionable
  • Own the packaging, security, observability, and upgrade machinery to deploy our product into customer environments โ€” cloud, VPC, on-prem, and restricted networks
  • Create product demos and prototypes with and for prospective customers, iterating rapidly alongside go-to-market
  • Work directly in customer environments when needed: integrate with their data and systems, ship solutions on-site, and translate what you learn into requirements for research and product
  • Design the tooling and playbooks that let solutions built for one customer generalize to the next
What we're looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

  • Strong generalist software engineering skills across the stack: backend systems, APIs, cloud infrastructure (GCP, AWS, or Azure), and modern frontend frameworks
  • Experience deploying and operating ML systems in production, ideally across diverse or customer-controlled environments
  • Familiarity with containerization, orchestration, and infrastructure-as-code (e.g. Kubernetes, Docker, Terraform)
  • Comfort working directly with customers: scoping ambiguous problems, building demos under time pressure, and representing the company technically
  • Background in scalable model serving & deployment architectures and the systems around them
  • Owns deliverables end-to-end, from requirements through autonomous execution
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