6 of 6 Google JAX Jobs in London

experience. Strong expertise in deep learning, neural networks, and generative models (diffusion, GANs, transformers). Hands-on experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow). Advanced programming skills in Python. Solid understanding of model training, evaluation, and scaling. Strong problem-solving, analytical, and communication skills. Ability ...

ICLR. Hands-on experience applying deep learning models to complex, ideally temporal or financial, datasets. Expertise in Python and modern ML frameworks (TensorFlow, PyTorch, JAX, etc.). Prior experience in a commercial or industry setting post-PhD, ideally in a similarly data-driven domain. Why Apply Compensation and total ...

reinforcement learning Experience with agent frameworks, tool-use planning and workflow orchestration Systems-level fluency across data pipelines, training infrastructure and inference (PyTorch, JAX, TensorFlow) Strong MLOps discipline: CI/CD for models, offline/online evaluation, safety and guardrails The ability to influence at exec level and mentor ...

Skills Experience with rendering at scale (e.g. render farms) Knowledge of computer vision, particularly real-time systems Experience with machine learning frameworks (e.g. PyTorch, JAX) Familiarity with the Blender C++ codebase Experience with AI-assisted coding or agent-based workflows Strong visual judgment and attention to detail for high ...

generative models — architecture, training, and inference. Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies. Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code. You document experiments clearly and communicate trade‐offs crisply. Nice-to-Have: Robotics ...

frontier AI company backed by NVIDIA, building large-scale open-weight foundation models alongside researchers and engineers from DeepMind, OpenAI, Meta, Anthropic, and Google Brain. ⚡ What You’ll Do Optimise GPU performance and training efficiency across 1,000+ GPU clusters Improve utilisation, throughput, and reliability across distributed training infrastructure … accelerate large-scale model training 🔧 What They’re Looking For Deep GPU infrastructure/distributed systems experience Strong knowledge of CUDA, NCCL, PyTorch, DeepSpeed, JAX, Megatron-LM, vLLM, etc. Experience operating large-scale GPU clusters (1,000+ GPUs) Kubernetes, Slurm, or similar orchestration expertise BONUS: Experience working on NVIDIA ...