The posting
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 and hiring across mid, senior, lead and principal levels. 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, foundation models and LLM/agentic systems. We are now hiring across a broad range of ML skills, including model training, evaluation, optimisation, infrastructure and deployment.
As an ML Engineer at Rowden, you will contribute to, own or lead development effort on projects and products, depending on your experience and level. You will work from applied research through to production, developing and deploying AI systems that solve complex problems with real-world impact. Our work is broad, spanning edge and embedded deployment, model evaluation, performance optimisation, data pipelines, large-scale training and ML infrastructure all focused on bringing useful AI capability to edge and embedded environments. We are building a team with complementary strengths.
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.
This role offers hybrid working with a minimum of 3 days per week on-site at our Bristol HQ.
Candidates must be eligible for SC clearance.
More information about security clearance is available here: https://www.gov.uk/government/publications/united-kingdom-security-vetting-clearance-levels
Salary
Whilst we have advertised a salary band, for senior level roles and above, compensation is tailored to the scope of the role and the specific experience a candidate brings. For this role, we encourage applicants from outside of the advertised salary band to apply. We will 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
- Own and ship ML in production: take ideas from R&D to robust, maintainable deployments—often onto edge or embedded hardware.
- Train and adapt models: work on model development, fine-tuning, evaluation and optimisation for real-world use cases.
- Work at scale where needed: run and improve training and inference workloads across GPUs, including multi-GPU or multi-node environments, to support models that can perform reliably in constrained settings.
- Improve performance: profile, optimise and debug ML systems across model code, data pipelines, inference stacks and hardware constraints.
- Own evaluation quality: design evaluation pipelines, benchmarks, test sets and feedback loops that help us understand model behaviour before and after deployment.
- End-to-end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
- 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.
- Raise the bar: depending on level, mentor others, guide technical decisions and improve engineering standards across the team.
Key skills, experience and behaviours
Essential
- Proven delivery: experience building, training, evaluating, optimising or deploying ML systems for real-world use, ideally in demanding environments.
- Deep domain expertise: Strong capability in at least one major area of ML, such as optimisation, computer vision, sequence modelling, LLMs, probabilistic methods, model evaluation or large-scale training.
- ML & maths depth: Strong grounding in ML/DL (optimisation, generalisation, probability, model architecture) and the ability to reason about these trade-offs in production.
- Software development: Strong Python skills and good software engineering habits, including version control, testing, code review, debugging and maintainability.
- Interpersonal skills: strong communicator who can mentor, influence, and bridge technical and non-technical audiences.
- Education: Degree, postgraduate study or equivalent practical experience in machine learning, computer science, engineering, mathematics or a related technical field.
- Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity.
Desirable
- MLOps excellence: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation.
- Data engineering: proficiency with Databricks, Apache Spark, Delta Lake, MLflow, and SQL; experience integrating datasets and maintaining data quality.
- Model training and optimisation: experience with pre-training, fine-tuning, distributed training, inference optimisation or adapting models for constrained environments.
- Education: PhD in AI/ML/CS or related field.
Beneficial knowledge
- General tooling and platforms: Databricks, AWS, GCP, GitHub, Docker/Kubernetes, MLflow, Jira.
- Edge deployments: Nvidia Jetson (e.g. AGX Orin), Raspberry Pi, or other embedded accelerators.
- Distributed model training & infra: Pytorch DDP, FDSP and TorchTitan, Megatron, Slurm, Run:ai, DeepSpeed, Kubernetes, cloud or on-prem GPU clusters.
About you
You’ve built ML systems that persist—deployed in real settings, iterated over time, and improved through real-world feedback. You enjoy guiding others, keeping systems healthy, and making the complex understandable.
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!
