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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer (All Levels) - **Company:** Rowden - **Location:** Bristol, UK - **Salary:** £50,000.0 - £95,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, JIRA, Computer Clusters, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Software Debugging, Distributed Computing Environment, Github, Python (Programming Language), Machine Learning, Performance Tuning, Raspberry Pi, Software Engineering, SQL Databases, Systems Integration, Graphics Processing Unit (GPU), Google Cloud, Feature Engineering, Pytorch, Large Language Models, Apache Spark, Deep Learning, Model Validation, SC Clearance, Data Lakes, Kubernetes, Information Technology, Performance Monitor, Slurm, Machine Learning Operations, Software Version Control, Data Pipelines, Docker, Databricks - **Published:** August 22, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5852590604 ## About the Role 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. This role offers hybrid working with a minimum of 3 days per week on-site at our Bristol HQ. Candidates must be eligible for SC clearance., * Proven delivery: experience building, training, evaluating, optimising or deploying ML systems for real-world use, ideally in demanding environments. * Deep domain expertise: Strong capability in at least one major area of ML, such as optimisation, computer vision, sequence modelling, LLMs, probabilistic methods, model evaluation or large-scale training. * ML & maths depth: Strong grounding in ML/DL (optimisation, generalisation, probability, model architecture) and the ability to reason about these trade-offs in production. * Software development: Strong Python skills and good software engineering habits, including version control, testing, code review, debugging and maintainability. * Interpersonal skills: strong communicator who can mentor, influence, and bridge technical and non-technical audiences. * Education: Degree, postgraduate study or equivalent practical experience in machine learning, computer science, engineering, mathematics or a related technical field. * Builder mindset: bias to action, ownership over outcomes, and comfort working through ambiguity. Desirable * 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. * Model training and optimisation: experience with pre-training, fine-tuning, distributed training, inference optimisation or adapting models for constrained environments. * Education: PhD in AI/ML/CS or related field. Beneficial knowledge * General tooling and platforms: Databricks, AWS, GCP, GitHub, Docker/Kubernetes, MLflow, Jira. * Edge deployments: Nvidia Jetson (e.g. AGX Orin), Raspberry Pi, or other embedded accelerators. * Distributed model training & infra: Pytorch DDP, FDSP and TorchTitan, Megatron, Slurm, Run:ai, DeepSpeed, Kubernetes, cloud or on-prem GPU clusters. About you 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. ## Description We are growing our ML team and hiring across mid, senior, lead and principal levels. 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, foundation models and LLM/agentic systems. We are now hiring across a broad range of ML skills, including model training, evaluation, optimisation, infrastructure and deployment., As an ML Engineer at Rowden, you will contribute to, own or lead development effort on projects and products, depending on your experience and level. You will work from applied research through to production, developing and deploying AI systems that solve complex problems with real-world impact. Our work is broad, spanning edge and embedded deployment, model evaluation, performance optimisation, data pipelines, large-scale training and ML infrastructure all focused on bringing useful AI capability to edge and embedded environments. We are building a team with complementary strengths., * Train and adapt models: work on model development, fine-tuning, evaluation and optimisation for real-world use cases. * Work at scale where needed: run and improve training and inference workloads across GPUs, including multi-GPU or multi-node environments, to support models that can perform reliably in constrained settings. * Improve performance: profile, optimise and debug ML systems across model code, data pipelines, inference stacks and hardware constraints. * Own evaluation quality: design evaluation pipelines, benchmarks, test sets and feedback loops that help us understand model behaviour before and after deployment. * End-to-end ownership: data collection/curation, feature engineering, model training, evaluation, deployment, monitoring, and iteration. * 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. * Raise the bar: depending on level, mentor others, guide technical decisions and improve engineering standards across the team. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)