Platform Engineer

Cuspai; Cambridge; On-site; GBP 110,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Equity in CuspAI

28 days holiday (UK) + public holidays

Gold Standard parental leave

Professional development budget

Meaningful problems

Job summary

CuspAI is building a frontier AI platform in Cambridge to empower researchers in materials science. We design a cloud-native ML infrastructure that scales training and deployment, while enabling researchers to focus on science rather than systems.

You will own the ML infrastructure stack, ensure reliability, and collaborate across ML researchers, chemists, and software engineers to accelerate breakthroughs in energy and materials discovery.

Qualifications

• Excited to enable scientists to work on world-changing challenges.
• Tooling and infra builder, keeps up-to-date with relevant technologies and open source projects.
• Seasoned engineer with deep expertise in ML infrastructure, able to work autonomously.
• Deep experience with ML workloads on Kubernetes and multi-cloud platforms (AWS, GCP, neoclouds).
• Strong Python and/or Go skills for infra automation, not just scripting.
• Extensive IaC experience with Terraform, Helm, Kapitan and GitOps workflows.
• Experience with ML platforms for JAX and/or PyTorch; TensorFlow less relevant.
• GPU infrastructure experience managing clusters and performance optimization.

Responsibilities

• Design and implement a cloud-native ML infrastructure platform on Kubernetes for training, evaluation, and deployment.
• Build and maintain the end-to-end MLOps stack including CI/CD, model registries, and experiment tracking.
• Scale distributed ML training across GPUs with robust data pipelines and checkpointing.
• Ensure 99.9% platform uptime with monitoring, alerts and automated recovery.
• Manage multi-environment Kubernetes configs using Kapitan for consistent dev/stage/prod deployments.
• Optimize cloud costs while maximizing throughput for research teams.
• Create developer tools to streamline experiment-to-production workflows.
• Collaborate with ML researchers, chemists, materials scientists and software engineers.

Skills

Kubernetes expertise

Python/Go

IaC (Terraform)

Kapitan/GitOps

Multi-cloud (AWS/GCP)

GPU clusters

CI/CD pipelines

ML model training orchestration

DevOps mindset

Tools

Terraform

Helm

Kapitan

GitOps

Job description

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.

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
• ‘Gold Standard’ parental leave: 26 weeks (primary caregiver) and 12 weeks (secondary caregiver) at full pay
• we look after you and your family while we work on the most important materials discovery problems together
• Professional development budget: We invest in your career development so you can stay up to date with the latest industry knowledge or add to your skills to increase impact and growth
• Solve meaningful problems: See how your work has a direct impact on advancing materials science and s