Platform Engineer
About the job
- At least 4 years of experience in a DevOps, Platform Engineering, or Site Reliability Engineering role.
- Strong proficiency with at least one major cloud provider and its core compute, storage, networking, and identity and access management services.
- Hands-on experience with infrastructure-as-code and container orchestration such as Kubernetes or an equivalent.
- Solid experience building continuous integration and continuous delivery pipelines using GitHub Actions, GitLab CI, or similar tools.
- Proficiency in Python and Bash, with the ability to read and write code across a polyglot stack.
- Deep knowledge of Linux systems and strong networking fundamentals.
- Design, provision, and manage cloud infrastructure using infrastructure-as-code tools such as Terraform, Pulumi, or equivalent.
- Own GPU compute environments for model training and inference, including cluster configuration, job scheduling, and cost optimization.
- Build and maintain continuous integration and continuous delivery pipelines that support rapid model iteration, automated testing, and safe deployments.
- Support machine learning workflow orchestration, experiment tracking, training run management, and data pipeline reliability.
- Ensure reproducibility across research and production environments through containerization and rigorous environment management.
- Define monitoring, alerting, and incident response processes.
- Implement security best practices including secrets management, identity and access management, network segmentation, and vulnerability scanning.
- Build internal tooling and documentation that enables researchers to self-serve infrastructure.
- Experience managing GPU clusters and machine learning training workloads, including NVIDIA, CUDA, and distributed training.
- Familiarity with machine learning operations tooling, including MLflow and Weights & Biases for experiment tracking; Airflow, Prefect, and Argo for workflow orchestration; and DVC for data versioning.
- Background in scientific computing or high-performance computing environments.
- Prior experience at a deep-tech or computational-science company.
About the company
Diffractive Labs is a London-based company building an AI Material Scientist, an autonomous system that discovers new materials by learning from real-world experimentation. It pairs machine learning models, including graph neural networks, with robotic lab automation, so computational predictions and physical experiments continuously inform each other in a closed discovery loop. A team of experimental scientists, computational researchers, and platform engineers runs experiments at scale, replacing manual trial-and-error. What sets Diffractive Labs apart is closing the loop between simulation and lab work, letting AI models propose materials and validate them through automated experimentation. The goal is to accelerate materials discovery and expand the pace of scientific research.
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Total Funding
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Headquarters
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Founded
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Simplify's Take
What believers are saying
- Built a 2026 hiring pipeline across experimental, platform, and research teams.
- Active postings in September 2026 show continued execution, not shutdown.
- Autonomous lab automation targets expensive materials bottlenecks with immediate enterprise value.
What critics are saying
- Twelve open roles in June 2026 signal high burn before revenue.
- Raised about $5 million leaves little room for failed experiments or delays.
- Materials discovery commercialization remains unproven; one bad data cycle kills conviction.
What makes Diffractive Labs unique
- Diffractive Labs closes the loop between wet-lab experiments and AI materials models.
- Its 2026 hiring spans lab automation, DFT, GNNs, and machine learning.
- London-based team builds autonomous materials discovery, not a software-only proxy.
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Benefits
Flexible Work Hours
Remote Work Options
Company Equity