What We're Looking For
We are seeking a DevOps Engineer to build and own the infrastructure that underpins our AI-driven materials discovery platform. You'll work directly with world-renowned ML researchers and software engineers to accelerate real scientific breakthroughs by making model training, experimentation, and deployment fast, reliable, and reproducible.
This is a foundational hire. You'll set the patterns others build on.
You will be joining a small, highly ambitious team of world-renowned engineers, AI researchers, and materials scientists. We move fast and value people who are energised by that.
What You'll Do
Design, provision, and manage cloud infrastructure (AWS/GCP) using infrastructure-as-code; Terraform, Pulumi, or equivalent.
Own GPU compute environments for model training and inference, including cluster configuration, job scheduling, and cost optimisation.
Build and maintain CI/CD pipelines that support rapid model iteration, automated testing, and safe deployments.
Support ML workflow orchestration; experiment tracking, training run management, and data pipeline reliability.
Ensure reproducibility across research and production environments through containerisation and rigorous environment management.
Define monitoring, alerting, and incident response processes so the team can move fast without things silently breaking.
Implement security best practices: secrets management, IAM, network segmentation, vulnerability scanning.
Build internal tooling and documentation that lets researchers self-serve infrastructure without waiting on you.
Skills & Qualifications
4+ years in a DevOps, Platform Engineering, or SRE role.
Strong proficiency with at least one major cloud provider and its core services (compute, storage, networking, IAM).
Hands-on experience with infrastructure-as-code and container orchestration (Kubernetes or equivalent).
Solid CI/CD pipeline experience, GitHub Actions, GitLab CI, or similar.
Proficient in Python and Bash; comfortable reading and writing code across a polyglot stack.
Deep Linux systems knowledge and strong networking fundamentals.
A bias for building things properly the first time, even under early-stage constraints.
Nice to Have
Experience with GPU cluster management and ML training workloads (NVIDIA, CUDA, distributed training).
Familiarity with MLOps tooling:
Experiment tracking (MLflow, Weights & Biases).
Workflow orchestration (Airflow, Prefect, Argo).
Data versioning (DVC).
Background in scientific computing or HPC environments.
Prior experience at a deep tech or computational science company.
Why Join Us
Work directly on infrastructure that enables AI to make real scientific discoveries.
Shape how we build from day one, no legacy systems, no inherited mess.
Collaborate with world-class researchers across materials science and machine learning.
Diffractive is building the AI Material Scientist that autonomously learns from real-world experimentation to push the boundaries of scientific discovery. We're early, moving fast, and working on problems that genuinely matter.
You'll join a small, high-calibre team where your work has real impact from day one. We're London-based with a flexible approach to how and where you work. We offer competitive salary, generous equity and benefits. You'll have a real stake in what you build and in the company's overall success.
How to Apply
If you're excited about this role and believe you could thrive in it, we'd encourage you to apply even if you may not align with every part of the job description.
Diffractive is an equal opportunities employer. We are committed to creating an inclusive environment for all employees and welcome applications from people of all backgrounds, experiences, and identities.
If you require any adjustments or accommodations at any point during the interview process please let us know - we will be happy to help.
Hit the apply button below to submit your application. We are looking forward to hearing from you!
