Platform Engineer

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
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.

Your Impact
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.

What You Will Do

• 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

Must Have Skills and Qualifications

• 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 date with the latest relevant technologies, tools, and open source projects.

• You are a seasoned engineer with deep expertise in building and maintaining ML infrastructure (not model development), ideally in start-up environments. We need you to be able to hit the ground running and work autonomously as a subject matter expert, providing input and guidance around best practice where appropriate.

• You bring deep experience with ML workloads on Kubernetes and multi-cloud platforms (AWS, GCP, neoclouds) .

• You have strong Python and/or Go programming skills for infrastructure automation - not just scripting .

• Expert-level proficiency in Infrastructure as Code (IaC) using Terraform, Helm, Kapitan, and GitOps workflows .

• Experience operating distributed ML platforms for JAX and/or PyTorch (Tensorflow would be less relevant).

• You will also need to have GPU infrastructure experience, specifically managing GPU clusters and performance optimisation

• Previous experience as a Software Engineer before moving into DevOps / MLOps / ML Infrastructure, or at least writing code regularly (at work and/or in your spare time), as this should equip you with the kind of coding skills we’re looking for.

Bonus Points (But Not Critical)

• 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)

What We Offer

• A competitive salary: We value and reward impact and growth

• Equity in CuspAI: You have a stake in the success of the company

• Time off to stay fresh: 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays…

Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/cuspai/platform-engineer-london-424820)