Senior Machine Learning Engineer

Manchester / Hybrid / Remote – depending on candidate location. Candidates will be required to come to the Manchester office if required, but are flexible.

Our global client is building advanced behavioural intelligence technology that enables secure, adaptive digital identity. By analysing how people naturally interact with devices, their AI systems generate powerful authentication signals designed for real-world use at scale.

This is a high-impact opportunity to join a rapidly growing AI team and take ownership of designing, training, and deploying cutting-edge behavioural models and data pipelines.

As a Senior ML Engineer, you will design, build, and refine machine learning models that sit at the core of the company’s behavioural AI platform.

This is a hands-on role working with real-world sensor and interaction data, building predictive models over time-series and human behaviour data, and deploying models that make authentication decisions in production. You’ll collaborate closely with other AI engineers, as well as engineering and product teams, to ensure models are robust, efficient, and production-ready.

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• Develop, train, and evaluate deep learning models for behavioural authentication using time-series and human behaviour data
  
• Work with multimodal, event-driven sensor data, including accelerometer, gyroscope, touch dynamics, and device interaction signals
  
• Build and maintain data processing pipelines for irregular and asynchronous mobile sensor data
  
• Design and train predictive models on behavioural datasets
  
• Implement and experiment with modern architectures, including transformer-based and attention-driven models
  
• Design and run experiments to improve authentication metrics such as False Accept Rate (FAR) and False Reject Rate (FRR)
  
• Track experiments, models, and datasets using tools such as MLflow, ZenML, and structured experiment management workflows
  
• Prepare models for efficient on-device execution, balancing accuracy, latency, and mobile hardware constraints
  
• Deploy models for edge inference using CoreML and ONNX
  
• Work closely with mobile engineering teams to embed AI functionality into production SDKs
  
• Contribute to the evolution of large-scale behavioural modelling architectures and shared training infrastructure
  

Required

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• Strong hands-on experience building deep learning systems in PyTorch (beyond pre-trained models or high-level wrappers)
  
• Demonstrated experience working with time-series data and human behaviour data, ideally from sensors, user interactions, or wearables
  
• Experience building predictive models on real-world datasets, with an emphasis on model architecture, experimentation, and evaluation
  
• Experience implementing modern neural architectures, including transformers, attention mechanisms, custom heads, and positional encodings
  
• Comfortable managing reproducible ML workflows, experiments, and model versions using tools such as MLflow, ZenML, or similar
  
• Experience deploying machine learning models using cloud infrastructure (AWS preferred), including services such as SageMaker
  
• Strong Python skills, including modern tooling (e.g. uv or equivalent dependency/workflow management)
  
• A practical, delivery-focused mindset with experience taking models from research to production
  
• PhD in Machine Learning, Computer Science, Applied Mathematics, or a related field
  
• Experience with behavioural modelling, biometrics, authentication systems, or security-focused AI
  
• Background in human activity recognition, behavioural analytics, or gait analysis
  
• Exposure to on-device or constrained-environment deployment
  
• Familiarity with representation learning or self-supervised approaches
  
• Research background or publications in relevant domains
  
• Edge Deployment: CoreML, ONNX
  
• Data: Python, S3, multimodal sensor and time-series pipelines
  
• Collaboration: Git, JIRA, structured OKR methodology
  

You’ll join a small, growing AI team where engineers have genuine ownership and autonomy. You’ll be trusted to solve complex, open-ended problems, apply research-driven thinking, and build systems designed to ship at scale. The culture values curiosity, technical depth, and real-world impact.