
Real job — pulled straight from Rowden Technologies’s careers page · Verified July 16, 2026 · No reposts.
Job description
Rowden Technologies is hiring a Senior Machine Learning Engineer — a full-time, based in Bristol, UK role. Apply directly on Rowden Technologies's careers page below.
Senior Machine Learning Engineer
Department: Engineering
Employment Type: Permanent - Full Time
Location: Bristol, Somerset, Bristol, UK, Hybrid
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
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.
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.
Candidates must be eligible for SC clearance.
More information about security clearance is available here: https://www.gov.uk/government/publications/united-kingdom-security-vetting-clearance-levels
Key areas of responsibility
- 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.
Key skills, experience and behaviours
- 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, 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.
Working at Rowden
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Frequently asked questions
What skills are required for Senior Machine Learning Engineer at Rowden Technologies?
The required skills for Senior Machine Learning Engineer at Rowden Technologies include: Python, Rust, Machine Learning, Deep Learning, Data Engineering, LLM, MLOps, CI/CD, Databricks, Spark, MLflow, SQL, AWS, GitHub, Docker, Kubernetes, JIRA, RAG.
What is the seniority level for Senior Machine Learning Engineer at Rowden Technologies?
Senior Machine Learning Engineer at Rowden Technologies is a Senior level position.
How do I apply for Senior Machine Learning Engineer at Rowden Technologies?
You can view the full description and apply for Senior Machine Learning Engineer at Rowden Technologies on EchoJobs: https://echojobs.io/job/rowden-technologies-senior-machine-learning-engineer-engineering-r6dfu.