Applied ML Researcher; Generative Models

Applied ML Researcher (Generative 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.

Due to growth, we are seeking an experienced Applied ML Researcher (Generative Models) to join our growing team and build state‑of‑the‑art generative models to design new materials e 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.

Hiring timeline: We’re aiming to start interviewing for this role in January and would like to make an offer by mid‑Feb.

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.

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.

• Machine Learning Mastery:
  Deep experience designing, building and training generative machine learning models (ideally diffusion, flow models or VAEs).
• Engineering Capability:
  Proficient coder (Python, PyTorch/JAX) who can move quickly from idea to working prototype to integrated solution.
• Collaborative Spirit:
  Demonstrated ability to work well in a team, communicating complex technical concepts to colleagues from different scientific backgrounds.
• Mission Alignment:
  Genuine enthusiasm for using technology to do something good in the world and solve sustainability challenges.

• Domain Expertise:
  Knowledge of chemistry or material science, specifically a solid understanding of inorganic crystals and their structural properties.
• Training at Scale:
  E…