Principal Robotics Engineer

Architecting entire software systems, setting coding standards, leading complex performance optimisations, and making strategic language/library choices. You'll still code, but it's often at a higher level, defining interfaces or reviewing critical sections.

Architecting large-scale, multi-robot systems, making strategic decisions on DDS implementations (e.g., CycloneDDS vs. Fast DDS), and designing custom communication protocols for inter-system communication. You're defining the middleware strategy.

Leading the simulation strategy for the entire robotics programme. Developing and maintaining a 'digital twin' of the production environment for robust testing, synthetic data generation, and advanced reinforcement learning applications. You're ensuring our simulations are a true reflection of reality.

Designing and overseeing the organisation's version control strategy. Implementing and managing robust CI/CD pipelines (e.g., Jenkins, GitLab CI) for automated testing, deployment, and fleet management on robotic hardware. You're driving our DevOps culture.

Architecting deployment strategies for large robot fleets using container orchestration tools. Managing device plugins, resource allocation for hardware acceleration (e.g., GPUs), and ensuring seamless updates and rollbacks across hundreds of robots.

Designing the overall perception strategy for our robots, selecting between traditional CV, deep learning, or hybrid approaches. Evaluating and integrating specialised libraries like PCL (Point Cloud Library) and ensuring the perception pipeline meets performance and safety requirements.

• Director of Robotics and other Technical Directors
• Head of Product Management (Robotics)
• Hardware Engineering Leads
• Operations Leadership (for deployment and field feedback)
• Safety and Compliance Teams
• Other Principal Engineers across different technical domains

• Key technology vendors (e.g., sensor manufacturers, middleware providers)
• Academic research partners
• Industry standards bodies
• Potential acquisition targets for new technology

Critical within 12 months. The 'sim-to-real gap' remains a major bottleneck. Future Principal Engineers will need to lead the development of highly accurate digital twins and sophisticated synthetic data generation pipelines to accelerate development, testing, and AI training, reducing reliance on costly physical prototypes.

High-fidelity physics simulation (e.g., FEM, multi · Procedural content generation for diverse simulati · Domain randomisation techniques for synthetic data · Integration of real-world sensor data for digital · Metrics for quantifying sim-to-real fidelity and t

• This quarter: Deep dive into NVIDIA Isaac Sim's advanced features for physics and sensor modelling.
• Next 6 months: Lead a project to improve the fidelity of one of our existing digital twin environments, focusing on a critical sensor.
• Within 12 months: Develop a synthetic data generation pipeline for a new perception task, demonstrating improved real-world model performance.
• Within 18 months: Present a strategy for leveraging digital twins to reduce physical testing cycles by 50%.

Quick win: Experiment with generating varied lighting conditions and object textures in our current simulation environment to see how it impacts a simple object detection model.

Critical within 6-12 months. As robots become more autonomous and perform complex tasks, the ability to run sophisticated AI models directly on the robot (at the 'edge') with low latency and minimal power consumption is paramount. This requires deep expertise in model compression, hardware acceleration, and efficient inference.

Model quantisation and pruning techniques for edge · Hardware accelerators (e.g., NVIDIA Jetson, Google · Real-time operating systems (RTOS) and their integ · Optimised inference frameworks (e.g., TensorRT, Op · Strategies for over-the-air (OTA) updates for edge

• This month: Research the latest edge AI hardware platforms and their software stacks.
• Next quarter: Lead a small project to optimise an existing perception model for deployment on a Jetson Nano, focusing on latency and power.
• Within 6 months: Develop a robust OTA update mechanism for edge AI models on a prototype robot.
• Within 12 months: Define the architectural standards for edge AI deployment across our entire robot fleet, including security and monitoring.

Quick win: Take one of our existing Python-based ML models and attempt to quantise it for a smaller footprint, even if it's just a theoretical exercise.