Job Specifications
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.
The Role
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.
Key Responsibilities
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
What We’re Looking For
Required
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
Desirable
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
Tech Stack
ML / AI: PyTorch, MLflow, SageMaker, ZenML
Infrastructure: AWS, Kubernetes, Docker
Edge Deployment: CoreML, ONNX
Data: Python, S3, multimodal sensor and time-series pipelines
Collaboration: Git, JIRA, structured OKR methodology
Why You’ll Enjoy Working With Our Client
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.