5 of 5 CUDA Jobs in London

how to run video encoding, decoding, and transmission at scale (e.g. HLS, WebRTC, and FFMPEG). Accelerator experience. You've developed GPU kernels and/or ML compilers (e.g., CUDA, OpenCL, TensorRT Plugins, MLIR, TVM, etc). Real-time experience. You've optimized systems to meet strict utilization and latency requirements with tools such as Nvidia NSight. Embedded experience. More ❯

Nice to have requirements: Experience in finance, trading, or quantitative research (not required). Publications, competition results (e.g., Kaggle, academic ML contests), or open-source contributions. Familiarity with C++, CUDA, or low-latency systems. Here is why you should join our dynamic team: Opportunity to work at one of the world's leading algorithmic trading firms Engaging projects offering More ❯

storage systems into the existing datacenter environment. Collaborate with DevOps/Platform teams to validate cluster orchestration (Kubernetes, Slurm, Bright Cluster Manager, or equivalents). Validate firmware, drivers, NCCL, CUDA libraries, and container environments for production readiness. Deployment & Delivery Oversight Provide technical leadership across the full deployment life cycle. Partner with datacenter operations to ensure correct rack layouts, cabling … HGX/SuperPod architectures. Deep knowledge of InfiniBand and high-performance networking architectures. Experience with cluster orchestration: Kubernetes , Slurm, PBS, or similar. Familiarity with AI/ML workload requirements, CUDA, Docker/OCI containers, and NVIDIA software stacks (NCCL, CUDA Toolkit). Comfort with Linux systems engineering, hardware validation, and troubleshooting across compute/network layers. Soft Skills More ❯

Role Overview Mercor is engaging advanced CUDA experts who specialize in GPU kernel optimization, performance profiling, and numerical efficiency. These professionals possess a deep mental model of how modern GPU architectures execute deep learning workloads. They are comfortable translating algorithmic concepts into finely tuned kernels that maximize throughput while maintaining correctness and reproducibility, 2) Key Responsibilities Develop, tune, and … benchmark CUDA kernels for tensor and operator workloads. Optimize for occupancy, memory coalescing, instruction-level parallelism, and warp scheduling. Profile and diagnose performance bottlenecks using Nsight Systems, Nsight Compute, and comparable tools. Report performance metrics, analyze speedups, and propose architectural improvements. Collaborate asynchronously with PyTorch Operator Specialists to integrate kernels into production frameworks. Produce well-documented, reproducible benchmarks and … performance write-ups. 3) Ideal Qualifications Deep expertise in CUDA programming, GPU architecture, and memory optimization. Proven ability to achieve quantifiable performance improvements across hardware generations. Proficiency with mixed precision, Tensor Core usage, and low-level numerical stability considerations. Familiarity with frameworks like PyTorch, TensorFlow, or Triton (not required but beneficial). Strong communication skills and independent problem-solving More ❯

functions in C ATen. Build and validate Python bindings with correct gradient propagation and test coverage. Create "golden" reference implementations in eager mode for correctness validation. Collaborate asynchronously with CUDA or systems engineers who handle low-level kernel optimization. Profile, benchmark, and report performance trends at the operator and graph level. Document assumptions, APIs, and performance metrics for reproducibility. … plus. 4) More About the Opportunity Ideal for contractors who enjoy building clean, high-performance abstractions in deep learning frameworks. Work is asynchronous, flexible, and outcome-oriented. Collaborate with CUDA optimization specialists to integrate and validate kernels. Projects may involve primitives used in state-of-the-art AI models and benchmarks. 5) Compensation & Contract Terms Typical range More ❯