Machine Learning Engineer

2 weeks ago

Cambridge, United Kingdom CuspAI Full time

About Us

We are an AI company dedicated to building a platform to accelerate the discovery of next-generation materials addressing critical global challenges across sustainability and clean energy. By integrating advanced machine learning with material science and process design, we have set out to unlock the future of materials . This mission is made possible by our world leading technical team - led by Prof. Max Welling and Prof. Berend Smit alongside Prof. Geoffrey Hinton.


Location(s): Any of our Cambridge (UK), Amsterdam or Berlin sites.


Impact

As we continue to expand, we are seeking a highly motivated and experienced Machine Learning Engineer to join our world-leading AI and materials science team. In this role, you will play a crucial part in developing our cutting-edge ML-based software platform aimed at transforming materials science to address critical climate challenges. You will work closely with our AI researchers, materials scientists, and engineers to build and optimise our core platform.


Working in a highly creative and iterative environment, you will collaborate with leading experts across multiple disciplines to turn groundbreaking research into practical, scalable software solutions. We believe that close collaboration, creativity, and spontaneity within a team are essential to achieving extraordinary results. Therefore, this position is primarily on-site. We also believe that truly exceptional work thrives in a culture built on trust, empathy, kindness, open-mindedness, and curiosity.


As this is a newly created team and position, you will have the chance to shape your role and make a significant impact on our platform's development. Your experience in software engineering for machine learning applications will be essential in bridging the gap between cutting-edge AI research and real-world implementation in materials science.


What You Will Do

  • Be part of a word-class ML Research, Materials and Data Engineering team to design, develop, train, and evaluate cutting-edge ML models for materials discovery and optimization.
  • Develop tools to enable large-scale ML experiments across distributed computing systems, maximising the use of available resources.
  • Optimise model performance, scalability, and robustness for production use within our computational platform for materials analysis and prediction.
  • Create innovative tools for material property prediction and design in partnership with domain experts. Own the process from rapid prototyping to production-quality code.


Skills and Qualifications

Essential:

  • Proven experience developing, training and deploying large-scale ML models in distributed setups
  • Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and orchestration tools like Kubernetes for large-scale ML model training and deployment.
  • Expert understanding of modern Python and working knowledge of at least one other programming language.
  • Familiarity with ML research and development
  • At least 3 years experience of developing software in a team
  • Bachelor's degree or higher in Computer Science, Materials Science, Physics, Chemistry, or a related technical field; equivalent practical experience considered
  • Passionate about combating climate change and driven to make a significant impact
  • Strong team player with excellent communication skills, thriving in collaborative and interdisciplinary environments


Bonus:

  • Knowledge of materials science, computational chemistry, or climate-related technologies
  • Strong knowledge of C++, CUDA, or other relevant languages
  • Contribution to open-source projects with a scientific computing and/or machine learning focus
  • Experience collaborating with research teams to prototype and productionize ML models for materials science or related fields
  • Expertise in data structures and algorithms, especially those relevant to materials informatics
  • PhD in Computer Science, Materials Science, Physics, Chemistry, or a related technical field; or equivalent practical experience


We encourage you to apply even if you don't meet every qualification. If you're interested in this role, please don't hesitate to apply - your unique perspective and experiences are valuable to us. We are committed to building a diverse and inclusive team and to creating an environment where everyone feels welcome, heard, and valued.

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