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Robotics Engineer, Laboratory Automation

South San Francisco, CAOnsiteFull TimePosted 12 days agoVisa sponsorship available

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Required skills

  • founding engineer
  • automated biology lab
  • experimental data
  • drug discovery
  • drug safety
  • liver toxicity
  • AI agents
  • drug development
  • robotics
  • instrument control

Nice to have

  • drug toxicity prediction
  • human outcomes
  • AI models
  • superhuman drug hunters
  • cancer therapies
  • chronic diseases

Role summary

Axiom is seeking a Founding Robotics & Automation Engineer to build the software and robotics for an advanced automated biology lab. This role involves developing systems to control instruments, orchestrate workflows, integrate hardware, and capture experimental data. The ideal candidate will have strong Python, software engineering, robotics, and laboratory automation skills, with a focus on reliability and reproducibility. Experience with specific lab automation platforms and systems engineering is preferred. This is a hands-on role requiring the ability to bridge software, hardware, and biology to create cutting-edge experimental systems.

Description:

Build Axiom’s automated biology lab from the ground up, connecting robotics, instruments, and software into a reliable, high-throughput experimental platform. Work directly with scientists and ML researchers to turn complex biological assays into context for agents to accurately predict human drug outcomes.

About Axiom:

Axiom’s mission is to eliminate unexpected drug toxicity and unlock an abundance of ultra safe medicines. We are starting with the liver, where toxicity remains a leading cause of drug failure. Our high-throughput human liver safety platform combines advanced human cell systems with imaging, transcriptomics, mass spectrometry, and drug metabolism measurements. Our AI agents integrate these data with chemical structure, human exposure, and thousands of clinical reference compounds to predict whether a drug will be toxic in humans, why, and how to make it safer.

We work with seven of the world’s top twenty pharmaceutical companies to design safety into molecules from the earliest stages. This applied work feeds directly into our partnerships with leading frontier AI labs, turning real discovery challenges and experimental feedback loops into scientific skill trees and reinforcement learning environments that train increasingly superhuman scientists. We then deploy these frontier models to make cancer therapies safer, unlock treatments for chronic diseases that demand exceptional safety, revive promising drugs abandoned because of toxicity, and much more.

Our ultimate ambition is to build a superhuman AI agent that predicts how a drug will behave across all major human organ systems before the first patient receives it. With insanely accurate safety predictions, we aim to enable rapid, high-throughput testing of novel drugs in humans with zero risk of toxicity. This is the key to unlocking an abundance of ultra safe, effective medicines.

What you will do:

  • Build Axiom’s automated biology lab from the ground up, owning the software and integrations that turn individual instruments into a reliable, high-throughput experimental platform.

  • Build the software that controls laboratory instruments and robotics and coordinates experiments from sample preparation through data collection.

  • Integrate liquid handlers, robotic arms, incubators, imaging systems, sequencers, mass spectrometers, and other instruments into workflows that run with minimal human intervention.

  • Work directly with scientists to translate complex biological assays into automated protocols that preserve scientific intent and produce consistent results at scale.

  • Develop scheduling and execution systems that coordinate shared equipment, track samples, monitor progress, and recover safely from failures.

  • Make every experiment traceable by capturing instrument commands, sample histories, protocol versions, metadata, logs, timestamps, and raw data.

  • Identify and eliminate bottlenecks in throughput, reliability, and data quality through better scheduling, instrument utilization, and workflow design.

What we look for:

  • You’re an exceptional programmer with strong software engineering fundamentals and a track record of building reliable systems.

  • You can bridge software, robotics, hardware, and biology to build complete automated experimental systems.

  • You care deeply about reliability, reproducibility, and data quality. You catch details others miss and understand how small errors can compromise an experiment.

  • You question conventional approaches to laboratory automation and find better ways to run experiments.

  • You think in systems, understand how each step affects the whole workflow, and identify the bottlenecks that limit performance.

  • You’re comfortable at the lab bench, learning unfamiliar instruments, debugging hardware, and working through difficult integration problems.

  • You take ownership from concept to operation, turning ambitious ideas into working systems with minimal oversight.

  • You’re curious about robotics, software, biology, and drug discovery, and willing to go deep wherever the problem takes you.

  • You communicate clearly and work closely with scientists, engineers, and laboratory operators to build systems they can trust.

Technical skills we value:

  • Strong Python and software engineering skills, including API design, asynchronous programming, and distributed systems.

  • Experience with laboratory automation platforms and interfaces such as PyLabRobot, Hamilton VENUS, Tecan EVOware, Opentrons, SiLA 2, and instrument APIs.

  • Experience integrating robotics, motion control, and laboratory hardware into reliable automated systems.

  • Hands-on experience with liquid handlers, automated plate transport, incubators, robotic arms, and integrated workcells.

  • Familiarity with workflows such as high-throughput screening, high-content imaging, cell culture, sequencing, and mass spectrometry.

  • Experience building systems for experiment orchestration, resource scheduling, instrument communication, data capture, and monitoring.

  • Mechanical, electrical, and systems engineering skills for diagnosing failures, troubleshooting equipment, and integrating instruments.

  • Experience with LIMS, laboratory execution systems, and infrastructure that connects samples, protocols, and experimental results.

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