Top Tech Jobs & Startup Jobs in London

Build a high-throughput GPU-based simulation pipeline for robotics foundation model training, implement actuators/sensors/controllers, bridge sim-to-real with policy training, and develop real-to-sim asset pipelines in collaboration with robotics and asset teams.

Lead profiling and performance ownership of a hybrid raster/ray-traced renderer. Profile, diagnose, and optimize GPU/CPU rendering across platforms, build custom instrumentation, evolve hybrid paths, set technical direction, mentor senior engineers, and collaborate with hardware vendors on low-level GPU issues.

Full-cycle recruiter responsible for sourcing and closing elite Research Scientists, Software Engineers, and Robotics engineers. Partner closely with founders and technical leaders, build scalable sourcing and screening workflows (Ashby/ATS), run a fast, candidate-friendly hiring process, and use data and automation to optimize pipeline health and conversions.

Lead development of a high-performance robotics simulation platform and compiler stack. Design compute infrastructure and dataflows for physics simulation and foundation-model training. Implement JIT/LLVM-based GPU codegen, optimize compile/runtime performance, add differentiable programming support, collaborate with simulation engineers, and contribute to open-source ML compiler projects.

Design and implement embedded control stacks, motion planning, and trajectory optimization for dynamic robots. Build real-time state-estimation pipelines fusing heterogeneous sensors, formulate and solve optimal control problems (nonlinear MPC, convex/trajectory optimization), create modular sim-to-hardware software, and lead on-robot debugging, tuning, and validation of controllers.

Develop and optimize a learning-based robotic manipulation control stack; design and maintain low-latency teleoperation systems; train and deploy policies using reinforcement and imitation learning; diagnose control-pipeline latency and sim-to-real gaps; co-optimize simulation and real-world robot behavior; collaborate on building general-purpose Physical AI.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines, kernels, and networking. Implement low-level CUDA/cuDNN/Triton kernels, tune hardware-software interactions, and develop monitoring and debugging tools for large-scale training runs to maximize utilization and reduce time-to-convergence.

Design, build, and operate large-scale distributed systems to improve reliability, scalability, and efficiency. Automate toil, perform capacity planning and performance analysis, prevent incidents, and provide cross-team technical leadership to balance system health with product velocity.

Design and implement low-latency, high-throughput inference pipelines for on-device and GPU-cluster deployment. Optimize kernels, batching, quantization, memory and scheduling. Build distributed serving systems, integrate low-level CUDA/Triton code with high-level frameworks, and develop monitoring/debugging tools to ensure reliability and determinism.

Design, build, and maintain foundational frameworks and internal tooling to accelerate AI research workflows. Collaborate with teams to create modular, reusable production-ready systems, remove cross-team friction, improve development workflows, and promote high code quality through reviews, documentation, and training.