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 growth, we are seeking an experienced Applied ML Researcher (Generative Models)* to join our growing team and build SOTA generative models to design new materials at CuspAI.

Note that you would be joining as a ‘Member of Technical Staff’, but the indicative job title above hopefully helps to explain the nature of this role.

Your Impact

In this role you will be building novel generative models that transform how we design new materials, which is critical for accelerating the discovery of next-generation materials for energy and sustainability challenges.

Your main focus initially will be ideating and implementing generative models for inorganic crystals at the atomistic scale that can be effectively conditioned on complex target physical properties. You will also integrate these models into our core platform.

Over time, you will get involved in modelling materials at different length and time scales, other material classes, and end-to-end discovery campaigns.

What You Will Do

Generative Model Development

Develop and prototype new ideas for generative models of material candidates that can be effectively conditioned on multiple target properties. The initial focus will be on inorganic crystals at the atomistic scale.
Implement, train, and rigorously evaluate these models against scientific benchmarks.
Translate theoretical concepts from research papers into functional, high-performance code.

Integration & Engineering

Integrate your models into the wider CuspAI platform, ensuring they are robust, scalable, and accessible for material discovery workflows.
Collaborate with the software engineering team to adhere to best practices in coding, testing, and deployment.

Discovery Campaigns

Run material discovery campaigns that utilise your generative tools to identify promising candidates for real-world applications (e.g. carbon capture or battery materials).
Analyse the outputs of these campaigns to iteratively improve model performance and domain relevance.

Interdisciplinary Collaboration

Work together with the existing material generation team and the wider Cusp technical team, to align model capabilities with experimental realities.
Partner with computational chemists to understand the physical constraints and properties required for valid and novel materials generation.

Must Have Skills and Qualifications:

Machine Learning Mastery: You possess deep experience designing, building and training generative machine learning models (ideally diffusion, flow models or VAEs).
Engineering Capability: You are a proficient coder (Python, PyTorch/JAX) with the ability to "get things done" - moving quickly from idea to working prototype to integrated solution.
Collaborative Spirit: You have a demonstrated ability to work well in a team, communicating complex technical concepts to colleagues from different scientific backgrounds.
Mission Alignment: A genuine enthusiasm for using technology to do something good in the world and solve sustainability challenges.

Bonus Points (But Not Critical):

Domain Expertise: Knowledge of chemistry or material science, specifically a solid understanding of inorganic crystals and their structural properties.
Training at Scale: Experience training and evaluating machine learning models at scale.
Track Record: Relevant publications in machine learning, chemistry, or material science.
Location Preference: While we have multiple hubs, a willingness to work from our Amsterdam office is a plus for this specific team.

Additional Considerations

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

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.

CuspAI

Vacatures

Applied ML Researcher (Generative Models)