1 to 25 of 32 CUDA Jobs in England

compute workloads Infrastructure-as-code mindset: Terraform, Ansible, or equivalent; CI/CD pipelines (GitHub Actions, Jenkins, or similar) Experience with hardware-accelerated compute (CUDA/GPU workflows, profiling, performance tuning) — even if not on custom silicon Strong debugging and observability skills: distributed tracing, logging, metrics dashboards Ability ...

independently drive research projects from idea generation through experimentation and publication. Nice to Have Experience with classical reconstruction tooling (COLMAP etc). C++ or CUDA, and a feel for performance and on-device constraints. #J-18808-Ljbffr ...

fundamentals: models, optimisation, evaluation and scaling. Capable of designing, executing and reporting from ML experiments. Lower-level programming for hardware efficiency, e.g. C++/CUDA/Triton. Practical familiarity with hardware capabilities for deep learning – threads, caches, vector & matrix engines, data dependencies, bus widths and throttling. Practical familiarity with ...

independently drive research projects from idea generation through experimentation and publication. Nice to Have Experience with classical reconstruction tooling (COLMAP etc). C++ or CUDA, and a feel for performance and on-device constraints. Compensation & Benefits £96,300 - £107,000 + bonus/equity/benefits Location & Work Model ...

TensorFlow; comfortable with distributed training (DeepSpeed/FSDP/SLURM/K8s). Experience in deep learning, NLP or LLMs; bonus for CUDA or data‐pipeline chops. Strong software‐design instincts: testing, code review, CI/CD. Self‐starter, low‐ego, collaborative. Benefits France 💰 Competitive cash salary and equity ...

research goals with practical engineering constraints Strong problem-solving skills, results orientation, and excellent collaborative communication Reliable and predictable availability Bonus Points Expertise in: CUDA kernel development, Triton/Pallas/CuTe DSLs, PyTorch/JAX internals, XLA optimization, or hardware acceleration (FPGA/ASIC) Knowledge of reinforcement learning ...

lifecycle of models in a multi‐tenant, high‐availability system. Familiarity with building ML inference services, model serialization (e.g., ONNX), and GPU programming (CUDA). You've built or worked on custom storage or job‐queueing systems before and have the scars to prove it. Maybe ...

specific solutions (e.g., network stacks, bidding algorithms, data pipelines, etc.).* Familiarity with advanced computing, AI, and/or GPU acceleration platforms such as CUDA, Triton, NeMo* Track record in crafting and implementing systems for real-time processing and low-latency decision-making.* Experience working in customer-facing technical ...

Strong Preference For Experience integrating accelerator hardware (GPUs, FPGAs, ASICs, NPUs, or novel architectures) into customer inference workflows Familiarity with the NVIDIA inference stack - CUDA, TensorRT, Triton Exposure to disaggregated inference architectures, prefill/decode separation, or KV cache management Compensation & Benefits Highly Competitive Salary: We are not saying ...

range of hardware from resource constrained embedded systems through to cloud computing is desirable. As is any knowledge of GPU programming languages and frameworks (CUDA, ROCm, etc). Your future colleagues will be similarly highly skilled, with experience across industry and the drive to innovate. You will find yourself ...

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. Benefits Opportunity to work at one of the world's leading algorithmic trading firms Engaging projects offering accelerated responsibilities and ownership ...

PyTorch ecosystem Experience with inference optimisation techniques, including quantisation, speculative decoding and model parallelism across modern GPU architectures Strong software engineering skills, including Python, CUDA and building reliable systems for machine learning workloads Strong communication skills, with the ability to collaborate across research, infrastructure and engineering teams Why join ...

SLURM, PBS, or equivalent), MPI/NCCL tuning, and distributed training frameworks such as PyTorch, JAX, or DeepSpeed. Deep understanding of GPU software environments: CUDA, cuDNN, NCCL, driver stacks, and the tooling required to run large-scale AI training and inference workloads reliably in production. Hands-on experience optimising ...

USVs, UAVs, or autonomous marine shipping. Experience with multispectral or thermal sensor calibration and fusion (EO/IR, LiDAR, Radar). Deep familiarity with CUDA, TensorRT, PyTorch, and OpenCV #J-18808-Ljbffr ...

systems, including vector database management and semantic search optimization. Preferred Qualifications Experience in the insurance or financial services sector. Deep knowledge of GPU architecture , CUDA, and hardware-level performance optimization. Familiarity with Document Intelligence frameworks (OCR, layout analysis, and multimodal extraction). MUST be fluent in Mandarin OR Cantonese ...

plus; a strong background in mathematics is required. Alternatively, experience training models with strong math skills. Strong coding ability in Rust (preferred), CUDA, or C. Solid understanding of transformer architectures. A product mindset; you’ll build production‐ready products. Hybrid Remote with the London Office. What ...

training (fine‐tuning, alignment, reward modelling). Publications at top‐tier AI conferences such as ICML, ICLR, NeurIPS, CVPR, etc. Experience using HPCs and CUDA for training large‐scale models. The ability to translate research into a product vision and carry it through to delivery. Nice to have Computer ...

tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks. Fluency in life sciences or drug discovery is a plus but not required to be considered. Working Location & Compensation ...

apply advanced techniques, optimize training pipelines on HPC clusters, and integrate ML models into latency-sensitive production environments, using C/C++, Python, and CUDA across large-scale systems. #J-18808-Ljbffr ...

systems for quantitative finance. You will optimize training pipelines, deploy models to low-latency production environments, and work across C/C++, Python, CUDA, and related GPU technologies. Join a fast-paced, collaborative team focused on impactful projects that push the boundaries of AI research and its applications ...

skills: Extensive experience in building AI platforms covering model hosting/inference optimisation and fine-tuning pipelines (LLM experience strongly preferred) Strong Python and CUDA engineering; solid understanding of GPU/CPU architecture and HPC fundamentals Deep inference optimisation expertise: KV-cache, batching, quantisation (INT4/FP8/GPTQ ...

related field. In-depth knowledge of at least one modern 3D graphics or compute API and its associated software ecosystems (e.g. Vulkan, DirectX, OpenCL, CUDA). Demonstrable technical expertise in the optimisation of software targeting such ecosystems (e.g. game engines, visual effects, computer vision, AI). Experience with ...

exceptional academic record. Experience with HPC, supercomputing or large-scale distributed systems. Research experience involving computational workloads. Experience with parallel programming (MPI, OpenMP, CUDA). Knowledge of scheduling systems such as Slurm, PBS or LSF. Contributions to technical projects, open source or research communities. Experience working with advanced computing … Scale Computing, Scientific Computing, Numerical Computing, Computational Science, Computational Engineering, Performance Engineering, Performance Optimisation, Benchmarking, Profiling, Low Level Programming, C++, C, Python, Fortran, Rust, CUDA, GPU Computing, GPU Acceleration, Parallel Programming, MPI, OpenMP, Multithreading, Concurrency, Algorithms, Data Structures, Systems Design, Kernel Development, Networking, Storage Systems, Distributed Storage, Automation, Infrastructure ...

optimize the end-to-end ML stack: data pipelines, training loops, inference serving, and deployment. Design and implement GPU-accelerated components, including custom CUDA kernels where off-the-shelf libraries are not enough. Work closely with the founders to translate product requirements into concrete optimization goals and technical roadmaps. ...

will focus on throughput, accelerator utilization, and robust tooling, collaborating with researchers and engineers across teams. The role emphasizes low-level kernel work (CUDA/Triton), distributed training strategies, and cutting-edge ML infrastructure to drive efficient language understanding and generation. #J-18808-Ljbffr ...