Platform Engineer

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.

As we grow, we are seeking a Platform Engineer with strong Python coding, Dev Ops and cloud platform expertise to build and maintain the infrastructure that powers our ML research teams. You will be the architect of the systems that enable our researchers to train and deploy models at scale.

In this role, you will build and maintain the ML infrastructure platform that empowers our AI researchers and materials scientists to conduct cutting‑edge experiments.

You will help to build and maintain the entire ML operations stack - from cloud architecture to deployment pipelines - ensuring our research teams can focus on science while you handle the systems.

You will be part of a newly created team working with our VP ML Engineering, in which you'll have the chance to help shape our entire ML infrastructure strategy and make a significant impact on our platform's architecture.

• Build the ML Infrastructure Platform: Design and implement a cloud‑native platform on Kubernetes that enables researchers to easily train, evaluate, and deploy models without worrying about infrastructure complexity.

• Own the MLOps Stack: Implement and maintain CI/CD pipelines, model registries, experiment tracking systems, and deployment automation - the full lifecycle of ML operations.

• Scale Distributed ML Model Training: Build infrastructure to support distributed training across many GPUs, implementing solutions for data pipelines, checkpointing, and resource optimisation.

• Platform Reliability: Ensure 99.9% uptime for our ML platform through monitoring, alerting, and automated recovery systems. Be the guardian of infrastructure stability.

• Configuration Management: Use Kapitan to manage complex multi-environment Kubernetes configurations and ensure consistent deployments across dev, staging, and production.

• Cost Optimisation: Implement resource management strategies to optimise cloud spending and detect inefficient usage while maximising computational throughput for our research teams.

• Developer
  
  Experience:
  
  Create tools and abstractions that make it simple for researchers to go from experiment to production without deep infrastructure knowledge.

• Interdisciplinary
  
  Collaboration:
  
  Partner closely with ML Researchers, Chemists, Materials Scientists and Software Engineers to understand their needs, and to build the infrastructure for groundbreaking projects

• 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 Cusp is building.

• You're a builder of tools and infrastructure who enjoys making life as easy as possible for the teams. As part of your interest in this area, you stay up to…