MLOps Engineer in Oxford

The Opportunity

Lumai is redefining how the world computes. We are an ambitious, venture-backed UK startup pioneering a breakthrough AI accelerator for data centers which uses 3D optical compute. Our radical technology uses light to perform computation at orders of magnitude faster speeds and at far greater scales than ever before, all whilst consuming far less energy than traditional approaches. Lumai is unlocking performance and efficiency gains that could transform the economics of AI and compute infrastructure and reshape how intelligence scales globally. If you are passionate about bringing groundbreaking technology to market, and want to be part of a team pushing the boundaries of what is physically possible, Lumai is where you can make it happen.

About Lumai

Founded in 2022, Lumai is a University of Oxford spinout using optical processing to accelerate large language models (LLMs) and other transformer-based AI systems. The team combines expertise in optical computing, machine learning, and physics. Lumai has already secured over $15 million in investment from leading deep-tech investors and is scaling rapidly to deploy the fastest optical compute currently available globally.

The Role

We are building custom AI hardware and the full-stack software ecosystem to run it. As our first dedicated MLOps Engineer, you will own the infrastructure that takes models from research to silicon-validated production — designing, building, and operating the pipelines, tooling, and platforms that let our AI and hardware teams move fast without breaking things. This is a high-impact, high-ownership role at the intersection of ML research, compiler stacks, and novel hardware.

What You'll Do

• Design and operate end-to-end ML pipelines: data ingest, training, evaluation, quantisation, and deployment onto custom AI accelerator hardware.
• Build and maintain experiment tracking, model registry, and versioning infrastructure tuned to our hardware-in-the-loop workflows.
• Own CI/CD for ML: automated testing of model correctness, numerical accuracy, and on-chip performance after every change to models, compilers, or firmware.
• Develop and maintain tooling for benchmarking model inference on custom silicon, including latency, throughput, power, and utilisation metrics.
• Collaborate closely with ML researchers, compiler engineers, and hardware architects to identify and remove bottlenecks across the model-to-chip workflow.
• Instrument and monitor production inference deployments; design alerting and rollback strategies appropriate to hardware-accelerated serving.
• Manage compute resource scheduling across on-premises accelerator clusters and cloud (GPU/CPU) for training and simulation workloads.
• Drive infrastructure-as-code practices: containerisation, orchestration, and reproducible environment management.
• Contribute to the internal developer platform: self-service tooling, documentation, and runbooks that raise engineering productivity across the company.

What We're Looking For

Must-Have

• 5+ years of software or infrastructure engineering experience, with at least 2 years in an ML or AI-adjacent role.
• Strong Python skills and familiarity with major ML frameworks; comfortable reading and modifying model code.
• Hands-on experience building and operating ML pipelines in production: data pipelines, training orchestration, evaluation, and serving.
• Experience with experiment tracking and model lifecycle management tools.
• Solid understanding of containerisation and orchestration for distributed compute workloads.
• Infrastructure-as-code mindset; CI/CD pipelines experience.
• Experience with hardware-accelerated compute workflows, profiling, performance tuning.
• Strong debugging and observability skills.
• Ability to work effectively in a fast-moving, ambiguous environment.

Strong Preference For

• Experience with custom or novel accelerator hardware.
• Familiarity with ML compiler stacks.
• Experience with model optimisation techniques.
• Background in on-chip performance profiling and roofline analysis.
• Exposure to chip bring-up workflows.
• Contributions to open-source ML infrastructure or compiler tooling.
• Experience in a deeptech, semiconductor, or hardware startup environment.

Compensation & Benefits

• Highly Competitive Salary.
• Share Option Scheme.
• Pension Scheme.
• Private Health Insurance.
• Cycle to Work.
• L&D Allowance.
• Subsidised On-site Lunches.
• Holidays: 25 days paid holiday (plus bank holidays) per year.
• Socials: Be part of an inclusive community enjoying occasional all-company off-sites, lunches and socials.

Interview Process

Our process is four stages. An initial conversation with our HR team to understand what you want from the role and what we want from it. Two technical sessions with various members of our engineering team. Finally, an HR-team session covering scope, terms, and any final questions. We aim to move fast on candidates we are excited about; expect roughly three to four weeks end to end.

Lumai is an equal opportunity employer. We make hiring decisions on merit, scope-fit, and the strength of the working relationship we expect to build with each hire. Applications welcome from candidates of any background. If you are not sure whether you are a fit, send a note anyway.