
Principal ML Engineer
- London
- £150,000-180,000 per year
- Permanent
- Full-time
- Spearhead the design and refinement of machine learning models focused on understanding behaviour patterns and identifying cybersecurity anomalies.
- Partner with product, engineering, and domain experts to translate strategic goals and customer needs into practical, scalable ML solutions.
- Drive model development end-to-end, from exploratory analysis, feature design, and prototyping to validation and deployment.
- Collaborate with platform and infra teams to operationalize models and ship ML-powered features into production.
- Continuously assess and iterate on production models, balancing long-term ML strategy with tactical improvements.
- Champion code quality, observability, and resilience within their ML systems through reviews and hands-on contributions.
- Help shape their internal ML standards and practices, ensuring they stay ahead of industry advancements.
- Offer technical mentorship and be a thought partner to colleagues across data, ML, and engineering disciplines.
- Hands-on experience in developing and deploying machine learning models at scale.
- Deep familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs.
- Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus).
- Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning.
- Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.).
- Strong written and verbal communication skills, especially in cross-functional contexts.
- Exposure to large language models (LLMs) or foundational model adaptation.
- Previous work in cybersecurity, anomaly detection, or behavioural analytics.
- Familiarity with orchestration frameworks (Airflow or similar).
- Experience with scalable ML systems, pipelines, or real-time data processing.
- Advanced degree or equivalent experience in ML/AI research or applied science.
- Cloud platform proficiency (AWS, GCP, Azure).