Lead Machine Learning Engineer
Department:
Job Summary
About the role
Candidates must be eligible for Developed Vetting (DV) clearance.
More information about security clearance is available here:
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 persons 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 onhow we plan review share knowledge and shipand improve them as we grow.
- Grow the craft: raise the teams 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.
- 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 likeperformance architecture testing code review reproducibility evaluationand 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.
- 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.
- 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.
Required Experience:
IC
About Company
Rowden designs and builds systems, infrastructure, and applications to deliver mission advantage to those working to protect the security of the UK and its allies. We are setting a new standard in government technology provision: hyper-efficient engineering, better customer relationsh ... View more