Applied ML Technical Lead, Defence

About the job

Responsibilities

• Build ML capabilities for real Defence problems, including designing, building, training, and evaluating models for complex sensor data, signal processing, pattern recognition, classification, and multimodal intelligence.
• Develop and improve model architectures, training pipelines, evaluation frameworks, and inference systems using real-world and synthetic datasets.
• Translate research prototypes into reliable, deployable capabilities that operate within demanding technical and security constraints.
• Lead technical delivery from prototype to deployment by owning the technical direction and progress of applied ML projects.
• Turn operational objectives into concrete technical goals, experiments, milestones, and success measures, coordinating work across various teams and stakeholders.
• Identify technical dependencies, delivery risks, and blockers, and drive them to resolution.
• Establish clear evaluation criteria and demonstrate progress through measurable improvements and working capabilities.
• Balance hands-on engineering with planning and coordination for complex technical work.
• Engage with technical and operational stakeholders across the UK Defence and national security ecosystem to understand challenges and identify ML applications.
• Translate customer requirements and constraints into technical objectives, datasets, experiments, and delivery plans.
• Present technical progress, demonstrate working capabilities, and communicate results clearly to diverse audiences.
• Build trusted relationships with Defence stakeholders and act as a credible technical counterpart.
• Help customers understand the practical considerations of integrating and deploying ML systems in their existing environments.
• Deploy ML models into real-world environments, including secure, on-premise, and resource-constrained systems.
• Optimize model performance, inference, integration, and reliability.
• Develop robust approaches to evaluation, validation, and continuous model improvement.
• Understand data availability, access requirements, security restrictions, and deployment constraints.
• Establish feedback loops between operational users and engineering teams.
• Ensure built solutions are technically impressive and genuinely useful to end-users.

Requirements

• Background in applied machine learning, ML engineering, research engineering, or a similar technical role.
• Experience taking ownership of complex technical work.
• Proficiency in writing production-quality Python.
• Experience with modern ML frameworks such as PyTorch.
• Experience building, training, fine-tuning, and evaluating machine learning models.
• Experience working with complex datasets, designing experiments, and interpreting model performance.
• Understanding of trade-offs between model performance, compute requirements, latency, and deployment constraints.
• Experience with engineering infrastructure, including Git, containers, Linux, and GPU compute.
• Ability to take technical ownership of projects with multiple stakeholders, uncertain requirements, and evolving priorities.
• Clear communication of technical concepts to researchers, engineers, senior stakeholders, and operational users.
• Ability to move between model development, customer discussions, and delivery planning while maintaining technical depth.
• Ability to make sound technical judgments.
• Eligibility to hold UK Security Vetting Clearance SC or above.

Benefits

• Salary and Equity options available for discussion