Lumai - MLOps Engineer

Headington, Oxford, England, United Kingdom3mo ago

In Office Mid EMEA Developer Tools Semiconductors ML Engineer MLOps Python JAX MLflow Docker

Requirements

• 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 (PyTorch or JAX); 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 (MLflow, W&B, DVC, or similar) • Solid understanding of containerisation (Docker) and orchestration (Kubernetes or Slurm) for distributed compute workloads • Infrastructure-as-code mindset: Terraform, Ansible, or equivalent; CI/CD pipelines (GitHub Actions, Jenkins, or similar) • Experience with hardware-accelerated compute (CUDA/GPU workflows, profiling, performance tuning) — even if not on custom silicon • Strong debugging and observability skills: distributed tracing, logging, metrics dashboards • Ability to work effectively in a fast-moving, ambiguous environment where the hardware and software are both being built simultaneously • Strong Preference For • Experience with custom or novel accelerator hardware (FPGAs, ASICs, NPUs, or research chips) • Familiarity with ML compiler stacks: MLIR, LLVM, TVM, XLA, or vendor-specific compilers (NVCC, TensorRT, etc.) • Experience with model optimisation techniques: quantisation (INT8/INT4/FP8), pruning, distillation, or mixed-precision training • Background in on-chip performance profiling and roofline analysis • Exposure to chip bring-up workflows: running early software stacks on pre-silicon simulation or first-silicon hardware • Contributions to open-source ML infrastructure or compiler tooling • Experience in a deeptech, semiconductor, or hardware startup environment

Responsibilities

• 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 (e.g. MLflow, W&B, or equivalent) 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 (Kubernetes/Slurm), and reproducible environment management • Contribute to the internal developer platform: self-service tooling, documentation, and runbooks that raise engineering productivity across the company

Benefits

• Highly Competitive Salary: We are not saying our salary is a blank check, but let's just say it won't be a source of your stress • Pension Scheme: Plan for retirement with AVIVA • Pension Scheme: • Private Health Insurance: We firmly believe that you come first, and a happy you is a healthy you! Look after yourself and your loved ones with AXA • Private Health Insurance: • Cycle to Work: Spread the cost of a bike, a bike and accessories or just accessories and save on tax • Cycle to Work: • L&D Allowance: Stay at the forefront of your field with a £500 annual development budget • L&D Allowance: • Subsidised On-site Lunches: Enjoy on-site healthy meals at half the price, as Lumai covers 50% of the cost • Subsidised On-site Lunches: • Holidays: Enjoy some deserved "me time" with 25 days paid holiday (plus bank holidays) per year • Holidays: