Senior Machine Learning Engineer (ID: 3drmsXSl)

We are recruiting for a Senior Machine Learning Engineer on behalf of an innovative technology company developing advanced cybersecurity and network-monitoring solutions for Industrial Internet of Things (IIoT) and Operational Technology (OT) environments.

The company is building intelligent, autonomous monitoring infrastructure to protect critical industrial systems—including manufacturing facilities, utilities and energy infrastructure—from cyber threats, operational failures and abnormal network behaviour.

Machine learning sits at the heart of the platform, enabling it to distinguish genuine cyberattacks from benign operational anomalies and provide actionable security intelligence.

The Machine Learning Engineer Role:

We are seeking an experienced Senior Machine Learning Engineer to lead the development of the company’s Operational Technology Network Intrusion Detection System (NIDS).

This is a key technical position focused on transforming large volumes of semi-structured network telemetry, primarily in JSON format, into real-time security intelligence.

You will design, develop and deploy machine-learning pipelines capable of differentiating cyber threats from equipment faults and operational anomalies. Your work will directly contribute to improving the security, resilience and availability of critical industrial environments.

Why Consider This Opportunity?

• Meaningful impact: Help protect critical industrial infrastructure from cyberattacks and operational disruption.
• Complex technical challenges: Work on anomaly detection, behavioural analysis and zero-day threat detection using large-scale operational datasets.
• Greenfield development: Play a central role in building and scaling a new technology platform.
• Technical ownership: Influence the machine-learning architecture, data pipelines and production systems.
• Long-term opportunity: Join a growing business and share in its success through performance-linked equity.

Key Responsibilities:

• Machine Learning and Data Engineering
• Design high-performance parsers and data pipelines to ingest, flatten and process complex nested JSON network packets and industrial-protocol payloads.
• Engineer meaningful features from large volumes of network and operational telemetry.
• Build, train and validate unsupervised and semi-supervised anomaly-detection models.
• Develop time-series forecasting and deep-learning architectures for detecting previously unseen and zero-day threats.
• Create algorithms capable of distinguishing security incidents—such as lateral movement and unauthorised commands—from operational anomalies, including PLC misconfiguration, equipment drift and packet loss.
• AI Innovation and Feature Engineering
• Integrate explainable AI frameworks, such as SHAP or LIME, into the alerting engine to provide transparent, human-readable insights and support root-cause analysis.
• Use clustering and behavioural analysis to identify, profile and automatically catalogue network assets based on traffic metadata.
• Analyse historical telemetry to predict network-switch failures and bandwidth congestion before they affect operations.
• Apply traffic-flow analytics to recommend zero-trust firewall configurations and micro-segmentation policies.
• Explore and evaluate new machine-learning techniques relevant to network security and industrial environments.
• Production Deployment and MLOps
• Containerise and deploy resource-efficient models using technologies such as Docker and Kubernetes.
• Optimise models for deployment across industrial edge devices and centralised cloud environments.
• Establish and maintain robust MLOps practices using platforms such as MLflow, Weights & Biases or Kubeflow.
• Monitor model performance, model drift, concept drift and production degradation.
• Build reliable, testable and maintainable production systems in collaboration with the wider engineering team.

Essential Experience:

We are looking for someone with:

At least five years of professional experience developing and productionising machine-learning systems.
Strong commercial Python experience.
Advanced knowledge of NumPy, Pandas and Scikit-learn.
Hands-on experience with PyTorch, TensorFlow or both.
Experience developing production models using unsupervised or semi-supervised learning.
Strong experience processing and analysing large, complex or semi-structured datasets.
Experience with distributed or streaming technologies such as Kafka, Spark or Flink.
A proven track record of deploying machine-learning models into production.
A sound understanding of software-engineering principles, automated testing and maintainable production code.
Networking and Cybersecurity Knowledge

You should have a strong understanding of:

• TCP/IP;
• the OSI model;
• network flows and packet behaviour; and
• PCAP or packet-level network analysis.

Previous cybersecurity experience is highly desirable, particularly where machine learning has been applied to network monitoring, threat detection or behavioural analytics.

Desirable Experience:

Experience in one or more of the following areas would be particularly valuable:

• Network Intrusion Detection Systems (NIDS);
• cybersecurity or Security Information and Event Management (SIEM) product development;
• Industrial IoT or Operational Technology environments;
• industrial protocols such as Modbus, DNP3, BACnet, OPC UA or PROFINET;
• graph neural networks or graph-based behavioural modelling;
• time-series anomaly detection;
• explainable AI techniques, including SHAP or LIME;
• model optimisation for edge deployment;
• ONNX or TensorRT;
• Kubernetes-based machine-learning infrastructure;
• predictive maintenance; or
• industrial analytics.

Direct OT security experience would be advantageous but is not essential if you have strong machine-learning, networking and production-engineering experience.

Qualifications:

A bachelor’s degree, master’s degree or PhD in Computer Science, Machine Learning, Data Science, Cybersecurity, Mathematics, Engineering or another relevant quantitative discipline is desirable.

Equivalent professional experience will also be considered.

The Offer: 

• Salary from £50,000 per year.
• Performance-linked equity.
• The opportunity to shape the machine-learning architecture of a growing technology platform.
• Significant technical ownership and influence.
• The chance to work on AI-driven cybersecurity technology that protects critical infrastructure.

Additional Requirements:
You must be able to commute reliably to London EC3V or relocate before starting work.
You must have the right to work in the United Kingdom.

Position: Senior Machine Learning Engineer - Job Type: Full-Time - Salary: from £50,000 per year, plus performance-linked equity - Location: On-site, London, EC3V