Design, train, and deploy production-grade ML models.
About the Role
As a Senior Machine Learning Engineer, you will lead the design, development, and deployment of production-grade machine learning systems for global enterprise clients. You’ll work at the intersection of research and engineering, translating cutting‑edge papers into scalable, reliable solutions. This is a high‑impact role where you’ll mentor junior engineers, shape our ML architecture, and collaborate directly with clients to solve their most complex challenges.
- Design and implement end-to-end ML pipelines from experimentation to production
- Lead model development across NLP, computer vision, and time‑series forecasting
- Architect scalable model serving infrastructure on cloud platforms (AWS/GCP/Azure)
- Conduct rigorous model evaluation, A/B testing, and performance monitoring
- Mentor junior ML engineers and conduct technical code reviews
- Collaborate with data engineers to optimise feature stores and data pipelines
- Stay current with latest ML research and evaluate applicability to client projects
Requirements
- 5+ years of experience in machine learning engineering or applied ML research
- Strong proficiency in Python, PyTorch or TensorFlow, and Scikit-learn
- Experience deploying ML models at scale using Docker, Kubernetes, or serverless
- Deep understanding of ML fundamentals: optimisation, regularisation, evaluation metrics
- Experience with cloud ML services (SageMaker, Vertex AI, or Azure ML)
- Strong software engineering practices: version control, testing, CI/CD
- MSc or PhD in Computer Science, Machine Learning, or related field
Nice to Have
- Published research in top ML venues (NeurIPS, ICML, ACL, CVPR).
- Experience with MLOps tools: MLflow, Weights & Biases, DVC.
- Familiarity with distributed training frameworks (DeepSpeed, Horovod).
- Experience with LLM fine-tuning and prompt engineering.
- Contributions to open-source ML projects.
- Competitive salary with annual performance bonus.
- £2,000 annual conference and learning budget.
- Private healthcare and dental cover.
- Hybrid working — flexible office/remote split.
- 30 days annual leave plus bank holidays.
- Stock option scheme and pension matching.
- Latest hardware (M-series MacBook Pro, GPU workstation access).
About the Team
Our AI Research team is a tight‑knit group of 8 researchers and engineers passionate about pushing the boundaries of applied AI. We publish at top venues, contribute to open-source, and work on problems spanning healthcare diagnostics, financial risk modelling, and autonomous systems. You’ll have the freedom to explore new ideas while delivering real value to clients.
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