Senior Machine Learning Engineer

Department: Engineering

Employment Type: Permanent - Full Time

Location: Bristol, UK

Reporting To: Greg Daly

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 a Senior ML Engineer, you will be responsible for leading development effort on projects and products. You will design end‑to‑end solutions from early concepts to deployment, owning the quality of the software and ML solution. As a senior you will be expected to mentor colleagues in the team, maintain coding standards and act as a role‑model for good ML, data, and software practices and advocate for this in your work around the business.

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.

• 
  
• Own and ship ML in production: take ideas from R&D to robust, maintainable deployments, often onto edge or embedded hardware.
  
• End‑to‑end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
  
• Technical leadership: set direction, guide design, perform reviews, mentor teammates, and raise the engineering bar.
  
• 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.
  

• 
  
• Proven delivery: multiple years leading technical work that delivered measurable impact in production, especially on edge, embedded, or mission‑critical systems.
  
• ML & maths depth: strong grounding in ML/DL (optimisation, generalisation, probability, model architecture) and the ability to reason about these trade‑offs in production.
  
• LLMs & agentic systems: practical experience with prompt optimisation, retrieval/RAG, evaluation, and tool orchestration; aware of latency, cost, and reliability trade‑offs.
  
• 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.
  
• Software development: Strong python skills, experience with low‑level languages like Rust is desirable.
  
• Interpersonal skills: strong communicator who can mentor, influence, and bridge technical and non‑technical audiences.
  
• Education: MSc or equivalent experience required; PhD in AI/ML/CS or related field desirable but not essential.
  
• Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity.
  

• 
  
• 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.
  
• Operational practices: incident response, canary deployments, cost/performance optimisation across edge and cloud.
  

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.

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!