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, DevOps 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 will have the chance to help shape our entire ML infrastructure strategy and make a significant impact on our platform's architecture.

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• Experience using our tech stack (Flyte, Kapitan, Pants) or similar tools

• Experience with HPC environments and job schedulers (Slurm, PBS)

• Knowledge of ML serving infrastructure (Triton, TorchServe, KServe)

• Familiarity with scientific computing workflows and data management

• Experience with multi-tenant ML platforms

• Background in supporting research teams (understanding their unique needs vs. production ML)