76 to 82 of 82 CUDA Jobs in London

operate in significantly outpaces what we can currently build, meaning speed to power and reliability are critical to how we scale. This puts CUDA/GPU performance engineering at the center of how Fuse scales its compute infrastructure.ResponsibilitiesDesign, implement, and optimise CUDA kernels for high-throughput, latency-sensitive …/GPU baselines and drive continuous performance improvements.Contribute to internal libraries, documentation, and best practices for GPU performance engineering.Requirements4+ years of experience writing production CUDA code, or equivalent strong project/industry experience.Deep understanding of GPU architecture (SMs, warps, memory hierarchy, occupancy).Proficiency in C++ and CUDA; experience ...

will collaborate closely with researchers to co‐design and improve our models and shape the research agenda. Qualifications Strong engineering skills, especially in CUDA/Triton/Pallas/CuTe DSL kernel development, lower‐level PyTorch/JAX/XLA development, CUDA Graphs, FPGA/ASIC experience. ...

impactful on the business, and it will be challenging: this is a field with no easy or obvious solutions.QualificationsStrong engineering skills, especially any of: CUDA/Triton/Pallas/CuTe DSL kernel development, lower-level PyTorch/JAX/XLA development, CUDA Graphs, FPGA/ASIC experienceMust ...

Member of Technical Staff, Compute Infrastructure — Inherent (London) At Inherent, we are on a mission to build AI that recursively self‐improves to discover new knowledge. Scientific advances are the backbone of our economic, technological ...

Jump Trading Group is seeking world-class engineers to collaborate with our research, trading and engineering teams to build state-of-the-art ML systems for quantitative finance. You will work on training pipelines, low ...

energy and AI - optimising how power-dense GPU workloads are scheduled, cooled, and balanced against grid conditions in real time.We're looking for a CUDA Engineer to write and optimise the low-level GPU code that powers our inference workloads. You'll design custom CUDA kernels, tune performance … operate in significantly outpaces what we can currently build, meaning speed to power and reliability are critical to how we scale. This puts CUDA/GPU performance engineering at the center of how Fuse scales its compute infrastructure.ResponsibilitiesWrite and optimise custom CUDA kernels for core transformer inference operations.Profile ...

demand, Fuse is expanding into high-performance compute infrastructure that sits at the intersection of energy and AI. We're building the GPU/CUDA performance layer and the inference serving layer at the same time, from scratch - and we're looking for the founding engineer … Founding AI Inference Engineer to define and build how Fuse serves AI inference workloads at scale, reporting directly to the CTO. Where our CUDA and GPU engineering hires own kernel-level and hardware performance, this role owns the layer above it: how models actually get served, scaled, and delivered ...

demand, Fuse is expanding into high-performance compute infrastructure that sits at the intersection of energy and AI. We're building the GPU/CUDA performance layer and the inference serving layer at the same time, from scratch -- and we're looking for the founding engineer … Founding AI Inference Engineer to define and build how Fuse serves AI inference workloads at scale, reporting directly to the CTO. Where our CUDA and GPU engineering hires own kernel-level and hardware performance, this role owns the layer above it: how models actually get served, scaled, and delivered ...

automated software delivery in embedded systems. This is a deeply technical role for an engineer who has built large-scale foundation models, developed custom CUDA kernels, and scaled distributed ML systems in production. You'll own end-to-end development and deployment of a foundation model, optimise CUDA ...

about efficient large-scale training, low-latency inference in real-time systems and high-throughput inference in research. Part of this is improving straightforward CUDA, but the interesting part needs a whole-systems approach, including storage systems, networking and host- and GPU-level considerations. Zooming in, we also want … level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores and the memory hierarchy Debugging and optimisation experience using tools like CUDA GDB, NSight Systems, NSight Computesight-systems and nsight-compute Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN and cuBLAS Intuition about the latency and throughput characteristics ...