Lead MLIP Engineer for Next-Gen Materials Discovery in London

Lead MLIP Engineer for Next-Gen Materials Discovery in London

London Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Lead the development of machine learning models for advanced materials discovery.
  • Company: Jack & Jill, a pioneering company in London focused on innovative materials.
  • Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
  • Other info: Exciting hybrid role combining research and practical lab applications.
  • Why this job: Join a cutting-edge team and revolutionise materials discovery with your expertise.
  • Qualifications: Experience in machine learning and a strong understanding of physics principles.

The predicted salary is between 60000 - 80000 Β£ per year.

Jack & Jill in London is seeking a Machine Learning Engineer to lead MLIPs development, accelerating discovery of advanced functional materials by integrating ML with first-principles physics.

You will design scalable training pipelines, collaborate with physics teams to curate high-quality DFT datasets, and integrate MLIP workflows into larger discovery platforms for rapid screening of materials.

This hybrid role blends research with practical lab applications.

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Lead MLIP Engineer for Next-Gen Materials Discovery 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.

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Contact Details:

Jack & Jill Recruitment Team

We think you need these skills to ace Lead MLIP Engineer for Next-Gen Materials Discovery in London

Machine Learning
First-Principles Physics
DFT Datasets Curation
Scalable Training Pipelines
MLIP Workflows Integration
Materials Discovery
Collaboration Skills