Senior Machine Learning Engineer

Overview

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Senior Machine Learning Engineer

Department: Engineering

Employment Type: Permanent - Full Time

Location: Bristol, UK

Reporting To: Greg Daly

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 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.

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!

• 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: Strong foundation in computer science or related disciplines, gained through formal education or hands-on experience.
• Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity.

• General tooling and platforms: Databricks, AWS, Git Hub, 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,…