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About the Role

We are seeking a DevOps Engineer to build and own the infrastructure for an AI-driven materials discovery platform. You will work with ML researchers and software engineers to accelerate scientific breakthroughs by making model training, experimentation, and deployment fast, reliable, and reproducible. This is a foundational hire where you will set the patterns for future development.

Responsibilities

• 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 for rapid model iteration, automated testing, and safe deployments.
• Support ML workflow orchestration, including 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.
• Implement security best practices: secrets management, IAM, network segmentation, vulnerability scanning.
• Build internal tooling and documentation for researcher self-service infrastructure.

Requirements

• 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.

Skills

• Infrastructure-as-code
• Container orchestration (Kubernetes)
• CI/CD pipelines
• Python
• Bash
• Linux systems
• Networking fundamentals
• GPU cluster management
• ML training workloads
• MLOps tooling
• Experiment tracking (MLflow, Weights & Biases)
• Workflow orchestration (Airflow, Prefect, Argo)
• Data versioning (DVC)
• Scientific computing
• HPC environments

Location

• London-based

Work Type

• Flexible approach to how and where you work

Experience Level

• 4+ years

Salary/Compensations

• Competitive salary

Benefits

• Generous equity
• Benefits

About the Company

• 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.

Equal Opportunity

• 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.