Principal Machine Learning Engineer

Department: Engineering

Employment Type: Permanent - Full Time

Location: Bristol, Somerset, Bristol, UK, Hybrid

Key areas of responsibility

• Technical authority: take technical ownership of our most demanding ML projects, set the approach, and make the decisions that matter. 
• Stay hands-on at depth: design, build, train and debug yourself. This is a senior engineering role, not an advisory one. 
• Train and adapt large models: lead our large model work, including pre-training, fine-tuning, distributed training and evaluation at scale. 
• LLMs and agentic systems: bring practical expertise to projects across the team that build on LLMs and agents, with an eye on latency, cost and reliability in constrained settings. 
• From data to the edge: make sure our solutions work end-to-end, from data pipelines and training infrastructure through to deployment on edge hardware, with evaluation that tells us what a model actually does. 
• Represent Rowden externally: lead our technical presence at exercises, trials and customer engagements, and contribute technical expertise to bid writing. 
• Raise the bar: set what good looks like with our craft and technical leads, and mentor engineers through reviews, pairing and example. 

Key skills, experience and behaviours

• Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity. 
• Proven delivery: you have made decisions that mattered and lived with the outcomes. You have led technical work into production, or matured it to a high technology readiness level (TRL) ready for deployment. 
• Deep ML and DL expertise: mastery across modern machine learning and deep learning, with strong grounding in the maths and the ability to reason about trade-offs in production. 
• Large model training: deep, hands-on experience training and fine-tuning large models, including distributed training across multi-GPU or multi-node environments and writing highly performant model training code. 
• LLMs & agentic systems: a good level of knowledge and practical experience with LLM and agent-based systems—retrieval/RAG, tool orchestration, prompt optimisation and evaluation. 
• Data and edge experience: practical experience with data engineering and deploying to edge or embedded hardware, enough to design systems that work across the whole pipeline. 
• Software development: excellent Python skills; experience with low-level languages such as Rust or C++ is desirable. 
• Interpersonal skills: strong communicator who can influence without authority and bridge technical and non-technical audiences, including senior customers. 
• Education: MSc or equivalent experience required.

• Edge and embedded deployment: quantisation, pruning, distillation, compilation and runtime optimisation for constrained hardware. 
• Working with end users: experience at trials, exercises or demonstrations, or working directly with users in operational or other high-stakes settings. 
• MLOps: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation. 
• Education: PhD in AI/ML/CS or a related field. 

• Distributed model training & infra: PyTorch DDP and FSDP, Kubeflow, Slurm, Kubernetes, and a large-scale training framework such as DeepSpeed, Megatron or TorchTitan. 
• LLM/Agent tooling: evaluation harnesses, prompt optimisation, agent frameworks. 
• Inference and edge: TensorRT, ONNX Runtime, vLLM, Nvidia Triton Inference Server, KServe, Nvidia Jetson (e.g. Jetson Thor). 
• Data and platforms: Databricks, Apache Spark, MLflow, SQL, AWS, GCP, Docker. 

Working at Rowden