Senior Machine Learning Engineer (IIoT & OT Security)

Salary: £50,000 - 50,000 per year

Requirements:

• We require 5+ years of professional experience developing and productionising machine learning systems.
• We require strong commercial experience with Python.
• We require strong knowledge of NumPy, Pandas and Scikit-Learn.
• We require hands-on experience with PyTorch and/or TensorFlow.
• We require experience developing production ML models using unsupervised or semi-supervised learning.
• We require experience processing and analysing large or complex datasets, including semi-structured or nested data.
• We require experience with distributed or streaming technologies such as Kafka, Spark or Flink.
• We require experience deploying machine learning models into production environments.
• We require a good understanding of software engineering principles, testing and maintainable production code.
• We require a strong understanding of networking fundamentals, including 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.
• Experience in Network Intrusion Detection Systems, cybersecurity or SIEM product development, Industrial IoT or Operational Technology, 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 such as SHAP, model optimisation for edge deployment, ONNX or TensorRT, Kubernetes-based ML infrastructure, or predictive maintenance or industrial analytics would be particularly valuable.
• A Bachelors, Masters or PhD in Computer Science, Machine Learning, Data Science, Cybersecurity, Mathematics, Engineering or another relevant quantitative discipline is desirable.
• Equivalent commercial experience will also be considered.

Responsibilities:

• We design optimised parsers and data pipelines to ingest, flatten and feature-engineer complex, nested JSON network packets and industrial protocol payloads at scale.
• We build, train and validate unsupervised anomaly detection, time-series forecasting and deep learning architectures designed for zero-day threat detection.
• We develop algorithms capable of distinguishing security incidents such as lateral movement and unauthorised commands from operational anomalies such as PLC misconfiguration, equipment drift and packet loss.
• We integrate frameworks such as SHAP or LIME into the alerting engine to provide transparent, human-readable insights and root-cause analysis.
• We use clustering and behavioural analysis to fingerprint, profile and automatically inventory network assets based on traffic metadata.
• We analyse historical telemetry to predict network switch failures and bandwidth congestion before they impact operations.
• We use traffic-flow analytics to recommend zero-trust firewall configurations and micro-segmentation policies.
• We containerise and deploy resource-efficient models using technologies such as Docker and Kubernetes for industrial edge and centralised cloud environments.
• We establish and maintain MLOps practices using platforms such as MLflow, Weights & Biases or Kubeflow, with a focus on model drift, concept drift and performance degradation.

Technologies:

• AI
• Cloud
• Docker
• Firewall
• Flow
• Flink
• IoT
• JSON
• Kafka
• Kubeflow
• Kubernetes
• Machine Learning
• MLflow
• MLOps
• Network
• PLC
• Profinet
• PyTorch
• Python
• Security
• Spark
• TCP/IP
• TensorFlow
• numpy
• pandas

More:

We are recruiting on behalf of an innovative technology company developing advanced cybersecurity and network-monitoring technology for Industrial IoT (IIoT) and Operational Technology (OT) environments. We are building intelligent, autonomous monitoring infrastructure designed to protect critical industrial systems, including manufacturing environments, utilities and energy infrastructure, from cyber threats, operational failures and abnormal network behaviour. This Senior Machine Learning Engineer role is a key technical position focused on our Operational Technology Network Intrusion Detection System (NIDS), transforming large volumes of semi-structured network telemetry into real-time and actionable security intelligence. The role offers high-impact work, technical challenge, a greenfield environment and significant technical ownership across our ML architecture, pipelines and production systems. We offer a competitive salary package with performance-linked equity, and the role is based in London EC3V 9BS with in-person work.

last updated 37 week of 2026