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Machine Learning Engineer (Mid to Principal)
Rowden Technologies · Bristol
LeadHybrid
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. Train and adapt models: work on model development, fine-tuning, eva
Skills
Essential - 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.
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