1 to 25 of 31 Google JAX Jobs in the City of London

language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modeling workflows. ML Ops & deployment More ❯

language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modeling workflows. ML Ops & deployment More ❯

expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modelling workflows. Model development & deployment More ❯

expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modelling workflows. Model development & deployment More ❯

Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

algorithms, optimisation, and model evaluation 📈 Strong mathematical reasoning and analytical problem-solving skills 🗣 Excellent communication skills and a genuine passion for learning and innovation Nice to Have: 💾 Experience with JAX , Hydra , or Weights & Biases 📊 Exposure to GANs , diffusion models , or autoregressive architectures 📚 Knowledge of data privacy , machine unlearning , or secure ML methods 💻 Internship or academic project experience in AI More ❯

algorithms, optimisation, and model evaluation 📈 Strong mathematical reasoning and analytical problem-solving skills 🗣 Excellent communication skills and a genuine passion for learning and innovation Nice to Have: 💾 Experience with JAX , Hydra , or Weights & Biases 📊 Exposure to GANs , diffusion models , or autoregressive architectures 📚 Knowledge of data privacy , machine unlearning , or secure ML methods 💻 Internship or academic project experience in AI More ❯

Collaborate cross-functionally with hardware, software and research teams. Requirements Strong background in machine learning algorithm development and optimisation. Proficiency in Python and modern ML frameworks such as PyTorch, JAX or TensorFlow. Experience with data modelling, preprocessing and performance benchmarking. Understanding of ML evaluation metrics and model analysis methods. Excellent communication skills and collaborative mindset. 3+ years’ experience in More ❯

Collaborate cross-functionally with hardware, software and research teams. Requirements Strong background in machine learning algorithm development and optimisation. Proficiency in Python and modern ML frameworks such as PyTorch, JAX or TensorFlow. Experience with data modelling, preprocessing and performance benchmarking. Understanding of ML evaluation metrics and model analysis methods. Excellent communication skills and collaborative mindset. 3+ years’ experience in More ❯

Engineering or related quantitative fields. Background in wireless communication systems, e.g., OFDM and MIMO technologies. Track record of related publications or successful projects. Proficiency in Deep Learning frameworks (PyTorch, JAX). Familiarity with advanced AI methods (e.g., transformer models). Excellent scientific communication and teamwork skills. Enthusiasm for innovative research with real-world impact. Join us to shape the More ❯

Engineering or related quantitative fields. Background in wireless communication systems, e.g., OFDM and MIMO technologies. Track record of related publications or successful projects. Proficiency in Deep Learning frameworks (PyTorch, JAX). Familiarity with advanced AI methods (e.g., transformer models). Excellent scientific communication and teamwork skills. Enthusiasm for innovative research with real-world impact. Join us to shape the More ❯

VLMs, or image/video 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 or autonomous driving experience. RL for More ❯

VLMs, or image/video 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 or autonomous driving experience. RL for More ❯

Experience 5+ years’ experience in machine learning with a focus on training and inference systems Strong programming expertise in Python and C++ or CUDA Proficiency with PyTorch, TensorFlow or JAX Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar) Background in real-time, low-latency ML pipelines Familiarity with cloud and orchestration technologies Contributions to More ❯

+ Discretionary Bonus + Equity We are a stealth-mode AI laboratory researching and developing Machine Learning models. The founding team consists of Cambridge graduates and former engineers at Google, Meta, Bloomberg, Microsoft and Amazon. We are backed by prominent investors from the US and the UK, including institutional VC funds and C-level executives of global technology companies. … models in a distributed environment. LLMs: Understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Proficiency in Python using PyTorch/TensorFlow/JAX framework. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker). Algorithms and data structures: Excellent More ❯

+ Discretionary Bonus + Equity We are a stealth-mode AI laboratory researching and developing Machine Learning models. The founding team consists of Cambridge graduates and former engineers at Google, Meta, Bloomberg, Microsoft and Amazon. We are backed by prominent investors from the US and the UK, including institutional VC funds and C-level executives of global technology companies. … models in a distributed environment. LLMs: Understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Proficiency in Python using PyTorch/TensorFlow/JAX framework. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker). Algorithms and data structures: Excellent More ❯

+ Discretionary Bonus + Equity We are a stealth-mode AI laboratory researching and developing Machine Learning models. The founding team consists of Cambridge graduates and former engineers at Google, Meta, Bloomberg, Microsoft and Amazon. We are backed by prominent investors from the US and the UK, including institutional VC funds and C-level executives of global technology companies. … a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP More ❯

+ Discretionary Bonus + Equity We are a stealth-mode AI laboratory researching and developing Machine Learning models. The founding team consists of Cambridge graduates and former engineers at Google, Meta, Bloomberg, Microsoft and Amazon. We are backed by prominent investors from the US and the UK, including institutional VC funds and C-level executives of global technology companies. … a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP More ❯

+ Discretionary Bonus + Equity We are a stealth-mode AI laboratory researching and developing Machine Learning models. The founding team consists of Cambridge graduates and former engineers at Google, Meta, Bloomberg, Microsoft and Amazon. We are backed by prominent investors from the US and the UK, including institutional VC funds and C-level executives of global technology companies. … a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP More ❯

+ Discretionary Bonus + Equity We are a stealth-mode AI laboratory researching and developing Machine Learning models. The founding team consists of Cambridge graduates and former engineers at Google, Meta, Bloomberg, Microsoft and Amazon. We are backed by prominent investors from the US and the UK, including institutional VC funds and C-level executives of global technology companies. … a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP More ❯

into their R&D team, building systems that push the limits of computer vision, deep learning, and autonomous vehicle/machinery. You’ll be joining brilliant minds from Tesla, Google DeepMind, NVIDIA and more. 💼 Role: Senior Machine Learning Engineer – Robotics 🌍 Location: London (onsite) 💸 Package: Negotiable + stock options What you’ll be doing Training policies via representation learning, behaviour … looking for 3+ years developing deep learning systems with shipped models or published research. Hands-on with Vision Language Models, LLMs or generative models. Strong Python + PyTorch/JAX skills (debugging, profiling, shipping). Passion for bridging the gap between simulation and reality. This is one of the most exciting and technically challenging roles I’ve see with More ❯

into their R&D team, building systems that push the limits of computer vision, deep learning, and autonomous vehicle/machinery. You’ll be joining brilliant minds from Tesla, Google DeepMind, NVIDIA and more. 💼 Role: Senior Machine Learning Engineer – Robotics 🌍 Location: London (onsite) 💸 Package: Negotiable + stock options What you’ll be doing Training policies via representation learning, behaviour … looking for 3+ years developing deep learning systems with shipped models or published research. Hands-on with Vision Language Models, LLMs or generative models. Strong Python + PyTorch/JAX skills (debugging, profiling, shipping). Passion for bridging the gap between simulation and reality. This is one of the most exciting and technically challenging roles I’ve see with More ❯

institutional VCs and prominent angel investors, giving them a solid two-year runway and plans for another upcoming raise. The founding team includes senior engineers and researchers from Meta, Google, and AWS , each with deep experience training and deploying large-scale models. Their mission is simple but ambitious: to develop the next generation of language and multimodal foundation models … data curation, versioning, and reproducible experimentation. You’ll Bring Strong experience with LLMs, generative, or multimodal models , ideally involving large-scale training or evaluation. Hands-on fluency in PyTorch, JAX, or TensorFlow , with experience in DeepSpeed, Megatron, FSDP, or similar . Understanding of distributed training, parallelism strategies , and scaling laws . Familiarity with post-training methods (RLHF, RLAIF, DPO More ❯

institutional VCs and prominent angel investors, giving them a solid two-year runway and plans for another upcoming raise. The founding team includes senior engineers and researchers from Meta, Google, and AWS , each with deep experience training and deploying large-scale models. Their mission is simple but ambitious: to develop the next generation of language and multimodal foundation models … data curation, versioning, and reproducible experimentation. You’ll Bring Strong experience with LLMs, generative, or multimodal models , ideally involving large-scale training or evaluation. Hands-on fluency in PyTorch, JAX, or TensorFlow , with experience in DeepSpeed, Megatron, FSDP, or similar . Understanding of distributed training, parallelism strategies , and scaling laws . Familiarity with post-training methods (RLHF, RLAIF, DPO More ❯