19 days ago
Bristol, United KingdomMid Level / Senior / Staff+
Responsibilities
- Own and ship ML systems from research through robust, maintainable production deployments, often on edge or embedded hardware.
- Train, fine-tune, evaluate, optimise, and adapt models for real-world use cases.
- Run and improve GPU-based training and inference workloads, including multi-GPU and multi-node environments.
- Profile, optimise, and debug model code, data pipelines, inference stacks, and hardware-constrained systems.
- Design evaluation pipelines, benchmarks, test sets, and feedback loops to assess model behaviour before and after deployment.
- Manage end-to-end data and ML workflows, including data collection, curation, feature engineering, training, deployment, monitoring, and iteration.
- Build MLOps and LLMOps capabilities including model CI/CD, containerisation, orchestration, experiment tracking, registries, safety guardrails, canaries, and performance monitoring.
- Establish batch and streaming data pipelines with strong curation, provenance, and reproducibility.
- Collaborate with software, systems, product, and customer teams, and mentor others or guide technical decisions depending on experience level.
Requirements
- Proven experience building, training, evaluating, optimising, or deploying ML systems for real-world use.
- Strong expertise in at least one major ML area such as optimisation, computer vision, sequence modelling, LLMs, probabilistic methods, model evaluation, or large-scale training.
- Strong grounding in machine learning and deep learning, including optimisation, generalisation, probability, and model architecture.
- Strong Python skills and sound software engineering practices including version control, testing, code review, debugging, and maintainability.
- Strong communication skills with the ability to mentor, influence, and explain technical topics to non-technical audiences.
- A degree, postgraduate study, or equivalent practical experience in machine learning, computer science, engineering, mathematics, or a related technical field.
- Experience with reproducible pipelines, model versioning, CI/CD, observability, automated evaluation, data engineering, or distributed model training is desirable.
- Experience with Databricks, Apache Spark, Delta Lake, MLflow, and SQL is desirable.
- A PhD in AI, ML, computer science, or a related field is beneficial.
- Candidates must be eligible for SC clearance; prior defence experience is not required.
Benefits
- Hybrid working with a minimum of three days per week on-site at the Bristol headquarters.
- Flexible working arrangements, including discussion of flexibility, part-time working requirements, and workplace adjustments.
- Inclusive workplace support through Rowden’s Disability Confident Committed status.
Tech Stack
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