You will lead the development of machine learning interatomic potentials (MLIPs) to accelerate the discovery of advanced functional materials. By integrating ML models with first-principles physics and automated experimental data, you’ll build high-fidelity simulations that translate complex molecular dynamics into actionable insights for a physical laboratory, bridging the gap between theoretical research and industrial production.
Machine Learning Engineer (MLIPs) at well-funded AI physical sciences startup
This is a rare chance to apply frontier AI to the physical world at a well-funded London startup by leading the development of machine learning interatomic potentials (MLIPs). You will work alongside world-leading researchers from top universities and industry labs to build models that drive actual physical discovery, moving beyond theoretical research into real-world production. If you have deep expertise in DFT, equivariant architectures, and material simulation, this role offers a unique opportunity to see your work deployed in advanced industries like robotics and renewable energy.
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Location
London, United Kingdom
Compensation
Not Disclosed
Company
Confidential company
Role overview
About the company
Well-funded AI physical sciences startup
What you will do
- Design and implement scalable pipelines for training and fine-tuning machine learning interatomic potentials (MLIPs) using PyTorch or JAX.
- Collaborate with physics and simulation teams to build high-quality datasets using Density Functional Theory (DFT) and optimize equivariant message-passing architectures.
- Integrate MLIP workflows into larger simulation and discovery platforms to enable rapid, large-scale screening of candidate materials for structural and functional applications.
Who this is a fit for
- A PhD in Physics, Materials Science, or a related field with deep expertise in solid-state physics and computational materials modeling.
- Proven experience training MLIPs and a strong understanding of training dynamics, loss landscapes, and generalization in chemical/physical systems.
- Advanced proficiency in Python and modern ML frameworks, combined with hands-on experience using DFT packages like VASP or Quantum Espresso.
Why this role is remarkable
- Opportunity to work at the intersection of frontier machine learning and physical sciences with a world-class team from top research institutions and industry leaders.
- Backed by elite global venture capital firms, the company is tackling high-impact climate and industrial challenges by modernizing the discovery process for critical materials.
- Unique hybrid environment where your research directly influences physical experiments in a high-throughput laboratory, ensuring your models solve real-world engineering problems.
How Jack & Jill work together
I get to know what you’re great at, then find roles you’d never find yourself.Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support.
I recruit from Jack’s network and make the intro when I spot a great match.And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate.
Jack gets to know what you're great at and what you want next, then searches 15 million jobs daily and helps you discover roles at companies like this.
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If your profile’s a match and Confidential company wants to meet, Jill will make the intro. In the meantime, Jack will send you excellent alternatives.