Senior Machine Learning Engineer

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

We are growing our ML team to support new projects and product developments. We are looking for AI builders; you will be working on developing and deploying AI systems to solve complex problems that have real-world impact. You’ll join an existing ML team that works in close collaboration with software, hardware and systems teams to get useful AI into the hands of users.

Our ML team works end-to-end, from R&D to deployment, across traditional ML, deep learning, data engineering and LLM/agentic systems. As a Senior ML Engineer, you will be responsible for leading development effort on projects and products. You will design end-to-end solutions from early concepts to deployment, owning the quality of the software and ML solution.

As a senior you will be expected to mentor colleagues in the team, maintain coding standards and act as a role-model for good ML, data, and software practices and advocate for this in your work around the business. 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.

Candidates must be eligible for SC clearance. uk/government/publications/united-kingdom-security-vetting-clearance-levels Own and ship ML in production: take ideas from R&D to robust, maintainable deployments, often onto edge or embedded hardware. End-to-end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration.

Technical leadership: set direction, guide design, perform reviews, mentor teammates, and raise the engineering bar. MLOps/LLMOps: CI/CD for models, containerisation/orchestration, experiment tracking and registry, model evaluation pipelines, safety guardrails, canaries, and performance monitoring. Cross-team collaboration: partner with software, systems, and product colleagues; simplify complex topics for other disciplines and customers.

Data foundations: establish pragmatic data pipelines (batch/stream) that make curation, provenance, and reproducibility first-class. We don’t expect anyone to be a 10/10 in every area, and you don’t need to tick every single bullet point to apply. What follows is a list of the skills and experience that we think matter most for this role.

Different strengths are welcome, and we recognise that people grow into roles like this - don’t let this list put you off! Proven delivery: multiple years leading technical work that delivered measurable impact in production, especially on edge, embedded, or mission-critical systems. ML & maths depth: strong grounding in ML/DL (optimisation, generalisation, probability, model architecture) and the ability to reason about these trade-offs in production.

LLMs & agentic systems: practical experience with prompt optimisation, retrieval/RAG, evaluation, and tool orchestration; aware of latency, cost, and reliability trade-offs. MLOps excellence: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation.