Lab Automation Engineer

Posted Yesterday
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London, Greater London, England, GBR
In-Office
Mid level
Artificial Intelligence • Machine Learning • Software • Nanotechnology
The Role
Build and maintain software and orchestration that turns a physical lab into an autonomous system: implement instrument drivers, coordinate robots and data pipelines, automate workflows, integrate new instruments, and ensure reliability, error handling, and reproducibility while collaborating with materials scientists and ML researchers.
Summary Generated by Built In

What We're Looking For

We are seeking a Lab Automation Engineer to build the software and systems infrastructure that turns our physical lab into a fully autonomous system. You will design and implement software drivers for different scientific instruments as well as the orchestration layer that connects instruments, robots and data pipelines into an continuous experimental workflow.

You will be joining a small, highly ambitious team of world-renowned engineers, AI researchers, and materials scientists. We move fast and value people who are energised by that.

This is a role for someone who is at home in both a codebase and a lab, who has felt the frustration of a poorly automated instruments and done something about it, and who is excited to build a first of its kind autonomous lab.

What You'll Do

  • Design and implement the orchestration layer that coordinates instruments, robotic handling, and data capture across our automated lab.

  • Write and maintain software drivers for scientific instruments, handling vendor APIs, serial protocols, and communication standards.

  • Automate experimental workflows that integrate robotics, instrument control and real-time data analysis

  • Leverage open-source lab automation frameworks (MADSci or equivalent) to fit our specific instrument suite and research needs.

  • Work directly with materials scientists and ML researchers to translate experimental protocols into reliable, reproducible automated pipelines.

  • Maintain and improve the reliability of running automated workflows, including error handling, logging, and recovery from instrument failures.

  • Evaluate and integrate new instruments and robotic systems as our lab capabilities expand.

Skills & Qualifications

  • Proven experience building automation systems for scientific laboratories, either in industry or at a research institution.

  • Strong Python skills; comfortable building production-quality software

  • Direct experience writing software drivers or integrations for scientific instruments, and an understanding of the challenges involved: inconsistent vendor documentation, brittle communication protocols, hardware edge cases.

  • Experience with lab orchestration frameworks such as MADSci or similar open-source or commercial systems.

  • Enough hardware fluency to diagnose whether a problem is in the software, the instrument, or the integration between them.

  • Experience designing automated workflows end-to-end: from sample handling through measurement, data capture, and handoff to downstream analysis.

  • Comfortable working in a lab environment and collaborating closely with scientists who are not software engineers.

Nice to Have

  • Background in materials science, chemistry, metallurgy, or a related physical science discipline.

  • Experience integrating robotic sample handling (liquid handlers, robotic arms, plate movers) into automated workflows.

  • Contributions to open-source lab automation projects.

  • Experience at a national laboratory (Argonne, Diamond, ISIS, or similar) or an automated biotech or material science platform company.

Why Join Us

  • Build the automation infrastructure for one of the most ambitious materials discovery programmes in the world.

  • Work at the frontier of autonomous labs, where your software directly shapes what science gets done.

  • Collaborate with world-class researchers across materials science and AI.

Diffractive is building the AI Material Scientist that autonomously learns from real-world experimentation to push the boundaries of scientific discovery. We're early, moving fast, and working on problems that genuinely matter.
You'll join a small, high-calibre team where your work has real impact from day one. We're London-based with a flexible approach to how and where you work. We offer competitive salary, generous equity and benefits. You'll have a real stake in what you build and in the company's overall success.

How to Apply

If you're excited about this role and believe you could thrive in it, we'd encourage you to apply even if you may not align with every part of the job description.

Diffractive is an equal opportunities employer. We are committed to creating an inclusive environment for all employees and welcome applications from people of all backgrounds, experiences, and identities.

If you require any adjustments or accommodations at any point during the interview process please let us know - we will be happy to help.

Hit the apply button below to submit your application. We are looking forward to hearing from you!

Skills Required

  • Proven experience building automation systems for scientific laboratories
  • Strong Python skills; comfortable building production-quality software
  • Direct experience writing software drivers or integrations for scientific instruments
  • Experience with lab orchestration frameworks such as MADSci or similar
  • Hardware fluency to diagnose software vs instrument vs integration issues
  • Experience designing automated workflows end-to-end from handling to data handoff
  • Comfortable working in a lab environment and collaborating with non-software scientists
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The Company
Year Founded: 2025

What We Do

Diffractive Labs is an AI-driven startup dedicated to building an 'AI Material Scientist' that autonomously learns from real-world experimentation to push the boundaries of scientific discovery. By pairing frontier AI with a high-throughput wet lab in a closed experimental loop, the company aims to unlock next-generation materials for high-impact problems, effectively moving past the limits of human-curated data to accelerate discovery.

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