At a Glance
- Tasks: Lead the development of machine learning interatomic potentials to revolutionise material discovery.
- Company: Exciting AI startup focused on physical sciences with elite backing.
- Benefits: Competitive salary, innovative projects, and a chance to tackle climate challenges.
- Other info: Dynamic hybrid environment with opportunities for hands-on research and career growth.
- Why this job: Join a world-class team and make a real impact in advanced materials research.
- Qualifications: PhD in Physics or Materials Science with expertise in ML and computational modelling.
The predicted salary is between 60000 - 80000 £ per year.
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.
Location: London, UK
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.
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.
The ideal candidate:
- 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.
Machine Learning Engineer (MLIPs) at well-funded AI physical sciences startup in London employer: Jack & Jill
At Jack & Jill, we pride ourselves on being an exceptional employer that fosters a dynamic and innovative work culture. Our team enjoys a range of benefits including flexible working arrangements, professional development opportunities, and a collaborative environment that encourages creativity and growth. Located in a vibrant area, we offer unique advantages such as access to cutting-edge technology and the chance to work with industry leaders in AI-driven marketing strategies.
