Responsibilities

• Train, evaluate and refine reinforcement learning policies for autonomous decision-making and coordination between multiple agents
• Adapt existing models and integrate them into established software and hardware architectures across different deployment environments
• Ensure learned policies operate reliably within embedded constraints, accounting for sensor inputs, actuator behaviour, processing capacity and real-time latency
• Optimize inference and execution for local hardware, enabling autonomous operation without dependence on cloud-based processing
• Validate deployed policies against practical system requirements and support their integration into operational autonomous platforms

Desired Skills and Experience

• Completed a degree in Computer Science, Artificial Intelligence, or similar
• 5+ years in Python programming, supported by initial experience in Rust
• Experience in Reinforcement Learning, particularly the optimisation and deployment of RL policies onto hardware, without cloud dependencies
• Experience in multi-agent systems
• Candidates must be clear of any government, defense, or security-related employment restrictions