Applied AI/ML Engineer (Materials Foundational Models)

About CuspAI

CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering. We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock. We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters. We’re on the cusp of the on-demand materials era. Join us.

The Role

Due to the need to scale our scientific intelligence and data curation capabilities, we are seeking an Applied AI/ML Engineer (Materials Foundational Models) to build the LLM-powered agents that automate knowledge discovery across materials science and chemistry. We're keeping an open mind when it comes to location - you can be based in any of our main offices in Amsterdam, Berlin, or Cambridge/London.

Your Impact

In this role you will be building the intelligent interface and data engine that transforms unstructured scientific literature and databases into actionable, structured knowledge, which is critical for accelerating the decision-making loop of our AI and scientific teams. One of your focus areas will be to optimise the use of existing LLMs, potentially pre-training/re-training, refining conversational agents, and building the software pipelines for data ingestion. You will also be responsible for building and training specialized foundation models on terabytes of scientific literature at massive scale, fundamentally enhancing our platform's multi-modal capabilities.

What You Will Do

• Data Engineering & Model Training at Scale

Implement and manage robust software pipelines to efficiently collect data from diverse sources and integrate newly obtained data back into our primary databases. Lead efforts in parallel training of multi-modal foundation models at scale (e.g. orchestrating parallel training across hundreds of GPUs and handling data in the order of Terabytes). Apply strong software engineering best practices to ensure the systems are scalable, reliable, and maintainable. Support with the deployment and integration of production-grade foundation models.

• Scientific Collaboration & Integration

Partner closely with the Chemists and Materials Scientists in our Experimental and Orchestration Agent teams to integrate computational and experimental workflows into one seamless, optimizing loop. Be proactive in learning the technical vocabulary of materials science and experimental chemistry to facilitate meaningful interactions with domain experts. Contribute to our mission by ensuring all system designs align with CuspAI's commitment to sustainability and world-changing challenges.

Must Have Skills and Qualifications

• You are someone who gets excited about the opportunity to enable scientists to work on world changing challenges in this domain, with a personal interest in the potential applications of the technology that CuspAI is building.
• Strong software engineering skills and a proven track record of building complex, high-performance systems at scale in a production or industrial environment.
• Direct experience with the infrastructure and techniques required for parallel training of large models (e.g. across at least 100+ GPUs) and managing terabytes of data.
• A deep interest in the connection between AI and materials science or chemistry, and a willingness to rapidly learn the scientific vocabulary of the discipline.
• Educational background (Master’s degree or PhD) in Computer Science, Machine Learning, or a related quantitative field, with 4-5 years of industry experience highly preferred for non-PhD candidates.

Bonus Points (But Not Critical)

• Prior experience applying machine learning models specifically to problems in materials science, chemistry, or molecular informatics.
• Familiarity with the structure and content of scientific literature databases and chemical data formats.
• Exposure to multi-modal training (e.g. text, vision, etc.) and how to combine these in a meaningful way.

Additional Considerations

This role could be based in our Cambridge, London, Amsterdam or Berlin offices, with the expectation of being in the office three days per week. Additionally, there may be regular travel required to our other offices for collaboration and project oversight.

What We Offer

• A competitive salary plus equity package so you have a stake in the success of the company.
• 28 days holiday.
• Professional development budget for scientific conferences and technical training.
• Opportunity to work at the forefront of AI-driven scientific discovery with world-class researchers.
• Direct impact on advancing materials science through cutting-edge technology.
• Collaborative environment bridging AI research, computational chemistry, and experimental science.

Join us in shaping the future of materials with AI. Together, we can create groundbreaking solutions for a more sustainable world.

CuspAI is an equal opportunities employer committed to building a diverse and inclusive workplace. We do not discriminate on the basis of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding), veteran status, or any other basis protected by applicable law. We actively encourage applications from all backgrounds and value the unique perspectives and contributions that diversity brings to our team. Please let us know If you require any specific adjustments during or after the interview process. We will do everything we can within reason to accommodate.