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

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 Craft Lead, you will lead and develop a team of ML engineers working across our projects and products. You will be responsible for their development, their performance and the quality of their craft, and for how the team works. You will spend roughly 70% of your time on hands-on ML engineering and 30% leading the team.

You will need real ML expertise of your own and still enjoy doing the work, but you will not be expected to match the depth of our principal engineers—your focus is the team and the craft. You will work closely with them and with our technical and delivery leads, and 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.

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.

Lead and develop your team: line manage your engineers, set clear objectives, support each person’s progression, and be accountable for how the team performs.
Set what good looks like: work with technical and delivery leads to define and uphold standards for ML engineering, from code quality and evaluation to documentation.
Build how we work: develop the ways of working, processes and systems the team relies on—how we plan, review, share knowledge and ship—and improve them as we grow.
Grow the craft: raise the team’s skills through mentoring, reviews and knowledge sharing, and bring in new practices where they help.
Resource and hire: put the right people on the right projects with engineering and business line leadership, and lead hiring for the ML team as it grows.
Stay hands-on: spend around 70% of your time on project work as an ML engineer, so your leadership is grounded in what the team is actually doing.

Essential:

Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity.
People leadership: experience line managing and developing engineers, with a track record of people who got better, took on more, or progressed because of your support.
Growing and resourcing teams: experience hiring during a period of growth, and balancing people across several concurrent projects or customers.
ML engineering: current, hands-on experience building, training, evaluating or deploying ML systems for real-world use, with strong Python and good software engineering habits.
Good engineering practice: a clear view of what good ML and software engineering looks like—performance, architecture, testing, code review, reproducibility, evaluation—and how to build it into a team.
Interpersonal skills: strong communicator who can mentor, influence, handle difficult conversations well, and bridge technical and non-technical audiences.
Education: MSc or equivalent experience required.

Desirable:

Breadth across ML: experience with LLMs and agentic systems, edge deployment or large-scale training, enough to support engineers working in those areas.
MLOps: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation.

Beneficial knowledge:

Ways of working: Agile delivery and engineering career frameworks.
General tooling and platforms: Databricks, AWS, GCP, GitHub, Docker/Kubernetes, MLflow, Jira.
Edge deployments: Nvidia Jetson (e.g. Jetson Thor), Raspberry Pi, or other embedded accelerators.

Rowden

Lead Machine Learning Engineer

About the Job

Requirements

Responsibilities

Desired Qualifications

About the Company

Rowden

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