Description
We’re building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience.
At Rowden, we design and integrate advanced systems and products that sense, connect, and protect data in challenging environments where quick decisions are vital. Our solutions use intelligent automation to enhance speed and efficiency and are built to be reliable and straightforward for critical operations in remote or high-pressure settings.
Headquartered in Bristol (UK), we combine modern engineering methods with cutting-edge commercial technology to create adaptable, mission-critical systems. We focus on solving the tough challenges that others overlook, ensuring our customers can operate effectively in an ever-changing world.
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 an ML Engineer, you will be contribute to projects and products, from applied research to delivering ML in production on edge deployments. You will commit to continual learning and developing your craft. You will be expected to maintain coding standards and follow ML, data, and software best practices.
No prior defence experience is required. We’re interested in people who are passionate about getting AI systems into the hands of end users, that deliver tangible value, whatever the sector. You should be curious, with a desire to learn, develop and stay at the cutting edge.
Key areas of responsibility
Build and ship: contribute to models and services from prototyping to production; write maintainable code, tests, and docs.
Experimentation: collect and curate data, engineer features, train and evaluate models, and iterate with measurable outcomes.
MLOps in practice: build and support training/serving pipelines, experiment tracking, CI/CD for ML, and basic observability.
Collaborate widely: work with software, systems, and product colleagues to deliver features effectively.
Share knowledge: pair with teammates, participate in code reviews, and contribute to a positive, pragmatic engineering culture.
Key skills, experience and behaviours
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!
Applied ML experience: typically 1–5 years developing and delivering ML systems.
ML fundamentals: solid grounding in core ML/DL methods and the maths that makes them work; you can reason about failure modes and trade-offs.
LLMs & agentic systems: some hands-on experience (e.g., RAG, evaluation, prompt tooling) and eagerness to deepen expertise.
MLOps foundations: containerisation, reproducible training, experiment tracking, model packaging/serving, basic observability.
Data engineering: experience with Databricks and its toolchain, Apache Spark, Delta Lake, MLflow, Unity Catalog, Databricks SQL, and Databricks Workflows.
Software development: Strong python skills, experience with low-level languages like Rust is desirable.
Product mindset & communication: you care about user outcomes and can explain decisions clearly to non-ML teammates.
Builder, not just theorist: you like turning ideas into running systems and iterating with feedback.
Beneficial knowledge
General tooling and platforms: Databricks, AWS, GitHub, Docker/Kubernetes, MLflow, Jira.
Edge deployments: Nvidia Jetson (e.g. AGX Orin), Raspberry Pi, or other embedded accelerators.
LLM/Agent tooling: DSPy, llama.cpp, vLLM, evaluation harnesses, prompt optimisation, agent frameworks.
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!
What matters to us?
- Our focus is on the end user. We exist to deliver the best possible outcomes for the users of our systems.
- Pace matters. The problems we solve are urgent.
- Our diverse skills and backgrounds make us better. Our team prides itself on being inclusive and multidisciplinary.
- We are radically honest. Saying what we mean, even when it isn’t easy.
- We are pragmatists. We provide realistic, focused solutions that get to the point.
- We improve continuously. We are relentless in our drive to make things better.
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