7 of 7 CUDA Jobs in London

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 ...

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 ...

architectures and model optimisation Excellent software engineering and problem-solving abilities Experience working with complex datasets and real-world machine learning challenges Desirable Skills CUDA TensorRT Triton Quantised model training Edge AI deployment Computer Vision Robotics or autonomous systems Advanced C++ development Synthetic data generation What We're Looking ...

Dutch, Swedish, Norwegian, Danish, or Finnish is a plus. Preferred: Familiarity with NVIDIA GPU architectures (H100, A100, H200) and the software stack around them: CUDA, NCCL, cuDNN. Working knowledge of high-performance networking concepts: InfiniBand, RDMA, RoCE, TCP/IP. Background working directly with AI labs, research institutions ...

MLOps architecture. Willingness to learn new parts of the ML tech stack – Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and model serving frameworks. Fluency in life sciences or drug discovery is a plus. Working Location & Compensation: This is an office‐based, hybrid position ...

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 ...

edge technology and real commercial impact in a high-growth environmen t. You’ll be working acro ss:GPU/accelerated compute (NVIDIA ecosystem, CUDA, et c.)Cloud + virtualisation (orchestration, provisioning, multi-tenant environmen ts)AI/ML workloads — training, inference, production scal ingData centre & high-performance network ...