- Location
- Bristol, Somerset
- Workplace
- Hybrid, Onsite
- Type
- Full-time
- Department
- Engineering
- Seniority
- Lead
- Education
- PhD
- Clearance
- Required
- Source
- Pinpoint
Description
Principal Machine Learning Engineer
Department: Engineering
Employment Type: Permanent - Full Time
Location: Bristol, UK
Reporting To: Greg Daly
Description
We’re building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience.
We specialise in turning advances in sensing, AI, and communications into operational capability for the edge, where connectivity may be degraded or denied. Our work focuses on accelerating the deployment of technology, improving decision-making for frontline teams, and protecting people and critical assets in demanding environments.
Headquartered in Bristol, Rowden employs around 200 people and operates over 20,000 square feet of engineering and manufacturing facilities. We have a growing international footprint and are one of Europe’s fastest-growing engineering businesses.
About the role
About the role
As part of this journey, we are growing our ML team rapidly. The team works end-to-end, from applied R&D to deployment, across deep learning, data engineering, large models and LLM/agentic systems. Our work spans R&D contracts with the MOD and wider government, deploying ML onto edge and embedded hardware, and the assurance and testing that shows those systems can be trusted.
As a Principal ML Engineer, you will be a technical authority on our most important ML work. This is a principal individual contributor role: you will take the technical lead on key projects, contribute technical expertise to bids, and represent Rowden’s ML capability with customers and partners, including at international exercises a few times a year.
This is a hands-on engineering role working closely with software, hardware, systems and RF engineers, and with our ML craft and technical leads. You will report to the Head of ML.
No prior defence experience is required. We’re interested in people who’ve built and deployed AI systems in demanding environments and are passionate about delivering tangible value to end users, whatever the sector.
While this role offers hybrid working options, it requires a minimum of 3 days per week on-site at our Bristol HQ, with occasional travel to customer sites and international travel a few times a year.
Candidates must be eligible for Developed Vetting (DV) clearance.
More information about security clearance is available here: https://www.gov.uk/government/publications/united-kingdom-security-vetting-clearance-levels
Salary
For senior roles, compensation is tailored to the scope of the role and the specific experience a candidate brings. Because that can vary significantly, we do not always publish a fixed salary band. We will, however, discuss compensation openly at the first stage of the process and can share an indicative range before either side invests significant time.
Candidates must be eligible for Developed Vetting (DV) clearance.
More information about security clearance is available here: https://www.gov.uk/government/publications/united-kingdom-security-vetting-clearance-levels
Salary
For senior roles, compensation is tailored to the scope of the role and the specific experience a candidate brings. Because that can vary significantly, we do not always publish a fixed salary band. We will, however, discuss compensation openly at the first stage of the process and can share an indicative range before either side invests significant time.
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
Essential:
- 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.
Desirable:
- 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.
Beneficial knowledge:
- 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
We are committed to building a flexible, inclusive, and enabling company. Our aim is to create a diverse team of talented people with unique skills, experience, and backgrounds, so please apply and come as you are!
We also recognise the importance of flexible working and support this wherever we can. We typically operate a flexible, hybrid-working model, with an average 3 days in the office each week (dependent on the role). We welcome the opportunity to discuss flexibility, part-time working requirements and/or workplace adjustments with all our applicants.
Rowden is a Disability Confident Committed company, and we actively encourage people with disabilities and health conditions to apply for our roles. Please let us know your requirements early on so that we can make sure you have everything you need up front to help make the recruitment process and experience as easy as possible.
Finally, if you feel that you don’t meet all the criteria included above but have transferable skills and relevant experience, we’d still love to hear from you!
Skills
PythonRustAWSGCPDockerKubernetesCI/CDSQLMachine LearningDeep LearningPyTorchSparkDatabricksData Engineering