Robotics Simulation Engineer, Infrastructure Optimisation for Physics Simulation Engines

We're pioneering advancements in robotics, and we need a skilled engineer to optimize the software and hardware infrastructure that powers our physics simulation engines. In this role, you'll design, tune, and scale the compute environments and software stacks that underpin high-performance robotics simulations, focusing on throughput, reliability, and cost-efficiency. This position is ideal for someone passionate about squeezing maximum performance out of systems—from bare-metal hardware configurations to containerized deployments—so that simulation workloads for complex robotic behaviors (such as locomotion, manipulation, or environmental interactions) run as fast and reliably as possible. You'll work independently and collaboratively to profile infrastructure bottlenecks, architect scalable deployment strategies, and implement hardware- and software-level optimizations (e.g., GPU cluster tuning, memory and I/O optimization, or workload orchestration). This is a chance to influence our robotics pipeline from the ground up, ensuring the infrastructure behind our simulations is efficient, resilient, and ready for real-world scale.

Responsibilities

Optimize compute infrastructure : Profile and tune the hardware stack (GPUs, CPUs, memory, networking, storage) on which simulation engines (e.g., Genesis, MuJoCo, Isaac Sim) run, maximizing throughput and minimizing latency for large-scale robotics workloads. Enhance software infrastructure : Optimize operating system configurations, driver stacks, containerization (e.g., Docker, Kubernetes), dependency management, and CI/CD pipelines to ensure simulation environments are reproducible, portable, and performant. Benchmark and monitor : Establish systematic benchmarking and monitoring practices to identify infrastructure-level bottlenecks (e.g., GPU utilization, memory bandwidth, I/O contention) and validate the impact of optimizations. Integrate with robotics workflows : Embed optimized infrastructure into broader development pipelines, supporting testing, validation, and iterative design with minimal friction for simulation engineers and researchers. Document and share best practices : Produce technical documentation, infrastructure-as-code templates, and operational playbooks to guide team adoption and ensure long-term maintainability.

Requirements

Bachelor's or Master's degree in Robotics, Electrical Engineering, or a related field; experience with HPC or high-performance infrastructure for simulation workloads is a strong plus. Hands-on experience optimizing systems for GPU-accelerated workloads (e.g., CUDA, driver tuning, GPU cluster management). Proficiency in C++ and Python, with experience in scripting for automation and infrastructure tooling. Strong understanding of Linux systems administration, networking, storage I/O, and parallel/distributed computing. Experience with version control (e.g., Git) and agile development practices. Excellent problem-solving skills and the ability to work proactively in a fast-paced environment. Fluency in English (written and spoken).