Applied AI/ML Engineer (Property Prediction Foundational Models)
CuspAI, Cambridge, England, United Kingdom
A new frontier AI company with a mission to unlock breakthrough materials faster than nature, using AI to identify the next generation of sustainable materials. Our founding team is comprised of world‑class researchers in AI, chemistry and engineering.
Role Overview
Due to the rapid scaling of our scientific intelligence and data curation capabilities, we are seeking an Applied AI/ML Engineer to build and apply multi‑modal foundation models to solve a broad range of materials discovery tasks.
Hiring timelines: Interviews start mid‑January, offers by mid‑February.
Position Focus
You will build and refine property prediction and material characterization models that accelerate discovery of world‑changing molecules. You will own the development, integration and evaluation of these models within customer workflows and evolve multi‑modal foundation models for increasingly complex scientific modalities.
Key Responsibilities
* Own the development, integration, and evaluation of property prediction models within customer workflows, ensuring reliable deployment and performance.
* Adapt and fine‑tune core foundation models for specific property prediction applications to meet high‑stakes scientific requirements.
* Contribute to the ongoing development of multi‑modal foundation models for molecular systems, designing architectures that handle diverse input and output modalities.
* Implement uncertainty‑quantification methods to support Bayesian optimization pipelines, helping scientists navigate the vast space of potential materials.
* Build robust learned representations that generalise across various downstream tasks.
* Apply strong software engineering best practices to ensure all systems are scalable, reliable, and maintainable.
* Support the deployment and integration of production‑grade foundation models into our core platform.
* Partner closely with internal chemists and materials scientists to integrate computational and experimental workflows into one seamless optimisation loop.
* Proactively learn the technical vocabulary of materials science and experimental chemistry to facilitate deep, meaningful interactions with domain experts.
* Contribute to our mission by ensuring all system designs align with CuspAI's commitment to sustainability and solving the world's most pressing physical challenges.
Must‑Have Skills and Qualifications
* Strong software engineering skills and a proven track record of building complex systems in a production or industrial environment.
* Significant experience building, training and evaluating relevant ML models (e.g. Graph Neural Networks, Transformers, or Generative Models).
* Educational background of Master’s degree or PhD in Computer Science, Machine Learning, or a related quantitative field.
* For candidates without a PhD, 4–5 years of industry experience in an ML or Software Engineering role is highly preferred.
* Deep interest in the connection between AI and materials science or chemistry, and a willingness to learn scientific terminology.
* Excited about enabling scientists to work on world‑changing challenges and personal interest in the potential applications of the technology CuspAI is building.
Bonus Points (But Not Critical)
* Prior experience applying machine learning models specifically to problems in materials science, chemistry or molecular informatics.
* Exposure to multi‑modal training and how to combine different modalities.
* Experience implementing uncertainty‑quantification methods or Bayesian optimisation pipelines.
* Experience adapting and fine‑tuning foundation models for specific scientific property prediction applications.
Additional Considerations
This role could be based in Amsterdam, Berlin, Cambridge or London, with the expectation of being in the office three days per week. Regular travel may be required to other offices for collaboration and project oversight.
Benefits
* A competitive salary plus equity package.
* 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.
EEO Statement
CuspAI is an equal‑opportunity 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.
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