Machine Learning Engineer (ID: sFr206cg)

Machine Learning Engineer – Mid, Senior, Lead & Principal required. We’re growing our Machine Learning team and hiring across mid, senior, lead and principal levels.

We’re looking for AI builders: people who want to develop, deploy and improve AI systems that solve difficult problems and deliver tangible real-world value.

You’ll join an established ML team working closely with software, hardware and systems engineers to turn promising ideas into useful, deployable capability. Our work covers the full ML lifecycle, from applied R&D through to production, spanning traditional machine learning, deep learning, data engineering, foundation models, LLMs and agentic systems.

We’re hiring across a broad range of ML disciplines, including model training, evaluation, optimisation, infrastructure and deployment. You don’t need to be an expert in everything. We’re building a team with complementary strengths and are particularly interested in people who can bring genuine depth in one or more areas.

Depending on your experience, you’ll contribute to, own or lead the technical delivery of projects and products. You’ll work from applied research through to deployment, building AI systems designed to perform reliably outside the lab.

A significant part of our work focuses on bringing useful AI capability into edge and embedded environments. That might involve optimising models for constrained hardware, designing robust evaluation frameworks, improving inference performance, building data pipelines or developing the infrastructure needed to train and deploy models at scale.

No previous defence experience is required. We’re interested in people who have built and deployed AI systems in demanding environments and who care about delivering something genuinely useful to the end user, whatever sector they come from.

More information about UK security clearance is available through the UK Government's security vetting guidance.

We therefore encourage strong candidates to apply even if their expectations sit outside the advertised range. We’ll discuss compensation openly at the first stage of the process and can provide an indicative range before either side invests significant time.

Key areas of responsibility

• Build and ship ML systems: take ideas from research and experimentation through to robust, maintainable production deployments, including deployment to edge and embedded hardware.
• Train and adapt models: develop, fine-tune, evaluate and optimise models for practical, real-world use cases.
• Scale training and inference: build and improve workloads across GPU environments, including multi-GPU and multi-node systems where required.
• Optimise performance: profile, debug and improve ML systems across model code, inference stacks, data pipelines and hardware constraints.
• Own model evaluation: design benchmarks, test sets, evaluation pipelines and feedback loops that give us a clear understanding of model behaviour before and after deployment.
• Work end-to-end: contribute across data collection and curation, feature engineering, training, evaluation, deployment, monitoring and continuous improvement.
• Develop MLOps and LLMOps capability: build reliable model CI/CD, experiment tracking, model registries, evaluation pipelines, containerised deployments, safety guardrails, canary releases and performance monitoring.
• Strengthen our data foundations: develop pragmatic batch and streaming pipelines that make data quality, provenance, curation and reproducibility first-class concerns.
• Collaborate across disciplines: work closely with software, hardware, systems and product teams, explaining complex ML concepts clearly to technical colleagues, customers and other stakeholders.
• Raise engineering standards: depending on your level, mentor other engineers, influence technical direction and help improve engineering practices across the team.

Key skills, experience and behaviours (Essential)

Real-world ML delivery: experience building, training, evaluating, optimising or deploying machine learning systems for practical use, ideally within technically demanding environments.Depth in at least one ML discipline: strong expertise in an area such as model optimisation, computer vision, sequence modelling, LLMs, probabilistic methods, model evaluation or large-scale training.
Strong ML fundamentals: a solid understanding of machine learning and deep learning principles, including optimisation, generalisation, probability and model architecture, with the ability to make sensible engineering trade-offs.
Software engineering: strong Python skills and good engineering practices around version control, testing, code review, debugging and maintainability.
Communication and collaboration: the ability to communicate clearly, work across disciplines and, at more senior levels, mentor and influence others.
Relevant technical background: a degree, postgraduate study or equivalent practical experience in machine learning, computer science, engineering, mathematics or another related technical discipline.
Builder mentality: you take ownership, move quickly and are comfortable making progress when the problem or requirements are not yet perfectly defined.

Desirable

• MLOps: experience building reproducible ML pipelines, model versioning, automated evaluation, CI/CD and observability.
• Data engineering: experience with technologies such as Databricks, Apache Spark, Delta Lake, MLflow and SQL, including integrating datasets and maintaining data quality.
• Model training and optimisation: experience with pre-training, fine-tuning, distributed training, inference optimisation or adapting models to resource-constrained environments.
• Advanced research background: a PhD in AI, machine learning, computer science or a related technical discipline.

Machine Learning Engineer Beneficial knowledge

Experience with some of the following would be useful, but we don’t expect candidates to have worked with all of them:

• Databricks
• AWS or GCP
• GitHub
• Docker and Kubernetes
• MLflow
• Jira
• NVIDIA Jetson platforms, including AGX Orin
• Raspberry Pi or other embedded accelerators
• PyTorch Distributed Data Parallel (DDP)
• PyTorch Fully Sharded Data Parallel (FSDP)
• TorchTitan
• Megatron
• DeepSpeed
• Slurm
• Run
• Cloud or on-premises GPU clusters

About you

• You’ve built ML systems that made it beyond a notebook or prototype.
• You understand that production ML means more than achieving a strong benchmark score. Models need to perform reliably, fit within real engineering constraints and continue improving as new data and feedback become available.
• You enjoy solving difficult technical problems, but you’re equally interested in whether the resulting system actually helps the people using it.
• At senior, lead and principal levels, you’ll also be comfortable guiding other engineers, shaping technical decisions and making complicated subjects understandable to people outside your immediate discipline.

Working with us

We’re committed to building a flexible, inclusive and enabling company where talented people from different backgrounds can do their best work.

Our multidisciplinary teams bring together people with different skills, experiences and perspectives, and we believe that makes the systems we build stronger.

We also recognise the importance of flexibility. We typically operate a hybrid working model, with an average of three days per week in the office, depending on the role. We’re happy to discuss flexible working, part-time arrangements and workplace adjustments during the recruitment process.

We are a Disability Confident Committed employer, and we actively encourage applications from people with disabilities and health conditions. If you require adjustments during the recruitment process, let us know as early as possible so we can make sure you have the support you need.

And if you don’t meet every requirement listed above but believe your experience and transferable skills could make you successful in the role, we’d still like to hear from you.

About Us - What matters to us?

• We focus on the end user. We exist to deliver the best possible outcomes for the people using our systems.
• Pace matters. The problems we work on are important and often urgent.
• Different perspectives make us stronger. We value inclusive, multidisciplinary teams made up of people with different skills and backgrounds.
• We are radically honest. We say what we mean, including when the conversation is difficult.
• We are pragmatic. We focus on realistic solutions that solve the problem rather than adding unnecessary complexity.
• We continuously improve. We’re always looking for ways to make our technology, processes and ourselves better.

Candidates must be eligible for SC clearance.

Position: Machine Learning Engineer - Job Type: Full-Time - Salary: £50,000 to £95,000 per year

Location: This role offers hybrid working with a minimum of 3 days per week on-site at our Bristol HQ.

We advertise a salary band for this role, but for senior positions and above, compensation will reflect the scope of the role and the experience you bring.