Senior Machine Learning Engineer – Cybersecurity (Belfast, UK - Hybrid)

Senior Machine Learning Engineer – Cybersecurity Belfast | Hybrid Our client is hiring a Senior Machine Learning Engineer to join a team building a new cybersecurity product from the ground up. This is an opportunity to work at the intersection of machine learning, cybersecurity and large-scale software engineering, helping shape a new product as it moves from concept into development. You’ll work in a fast-paced engineering environment, designing and implementing intelligent detection capabilities that identify abnormal behaviour across large data sets. You’ll also have a genuine voice in technical decisions, architecture and product direction. What you’ll be doing: - Design and develop machine-learning and statistical anomaly-detection capabilities. - Build production-grade services using Java. - Develop approaches for identifying behavioural anomalies where labelled data may be limited or unavailable. - Implement techniques including distribution divergence, robust z-scores, Earth Mover’s Distance, baseline modelling and drift detection. - Apply unsupervised techniques such as Isolation Forest and related approaches. - Build numerical and ML functionality using Java libraries such as Apache Commons Math, Smile, XGBoost4J or ojAlgo. - Work directly with columnar data stores to generate and compute features at scale. - Contribute to technical architecture and engineering decisions. - Collaborate closely with software engineers, product teams and other technical stakeholders. - Deliver high-quality, scalable software with a strong focus on customer experience. What we’re looking for: You’ll ideally have: - 5+ years of commercial software engineering experience. - Strong hands-on experience with statistical anomaly detection. - Experience applying unsupervised machine-learning techniques. - An understanding of how to evaluate anomaly-detection models when labelled data is unavailable. - Experience implementing numerical or statistical techniques directly in Java. - Experience working with high-volume or columnar data platforms. - Strong problem-solving and analytical skills. - Excellent communication skills and the ability to work effectively within an Agile engineering team. Particularly useful experience: Any of the following would be highly advantageous: - Cybersecurity, UEBA, fraud detection or telemetry scoring. - Multi-signal or ensemble scoring. - Behavioural analytics. - Gradient-boosted models such as XGBoost. - Security or threat-detection platforms. - Large-scale system integration. - Data-intensive SaaS products. Education: A degree, or equivalent experience, in a highly quantitative discipline such as: - Mathematics. - Physics. - Statistics. - Computer Science. - Engineering. - Another relevant quantitative field. Why join? This is a rare opportunity to join at the early stages of a new product and help define how machine learning is applied within a developing cybersecurity platform. You’ll have the chance to influence technical direction, solve complex real-world problems and build sophisticated detection technology rather than simply maintaining an established system.