Waymo★3.0 (61)· London

You will use Python, deep learning frameworks (JAX, TensorFlow/PyTorch), and C++ (preferred) to develop data-selection algorithms and scaling laws for foundation model training. The AI Foundations team builds machine learning solutions for autonomous driving, focusing on reinforcement learning, generative modeling, and robust evaluation to improve the Waymo Driver. You will report to a Senior Staff Software Engineer.

Work with Python, PyTorch (or JAX/TensorFlow), RL fine-tuning frameworks like TRL or verl, and distributed multi-GPU training. The Reinforcement Learning Team advances RL, Bayesian optimisation, AI agents, LLMs, and vision-language models, applying them to AI for science, chemistry, physics, and robotics while publishing at top venues.

DeepL★3.8 (4)· London

You will build and deploy state-of-the-art reinforcement learning pipelines at scale using Python, PyTorch, TensorFlow, or JAX, working with large compute clusters and ML infrastructure. As part of DeepL’s Foundation Model Task Adaptation team, you will post-train large (multi-modal) models to align them with human intent and enable capabilities like reasoning.

DeepL★3.8 (4)· London

You will work with Python, PyTorch/JAX/TensorFlow, distributed/multi-node training frameworks (FSDP, DeepSpeed), and LLM post-training methods (SFT, DPO, RLHF, PPO). You will lead research on model-steerability and reinforcement learning for DeepL’s next-generation LLM-based translation models at the scale of hundreds of billions of parameters. You will own the full lifecycle of model delivery, from prototyping and large-scale experiments to production deployment.

DeepL★3.8 (4)· London

The Senior Research Scientist will develop multimodal and vision models using Python and PyTorch/JAX/Tensorflow, focusing on post-training (supervised fine-tuning, knowledge distillation, reinforcement learning) and data-centric pipelines. As part of DeepL’s Language AI team, the researcher will own the lifecycle of models powering document translation and vision capabilities for the company’s language AI platform.

DeepL★3.8 (4)· London

Senior Research Scientist at DeepL owning foundational modelling for next-generation translation models. Work with Python, PyTorch/JAX/TensorFlow, LoRA, PEFT, and Mixture-of-Experts to select and scale open-weight foundation models to hundreds of billions of parameters. Collaborate with post-training, RL, and instruction-following specialists to drive models from prototype to production.

Jump Trading★3.8 (11)· London

Use Python, C++, PyTorch, TensorFlow, and JAX (plus CUDA/ROCm for HPC) to apply machine learning and deep learning research. The team of traders, quantitative researchers, and engineers uses statistical analysis and data mining to develop predictive trading models for global financial markets.

Jump Trading★3.8 (11)· London

Jump Trading seeks PhD/postdoc research scientists to apply machine learning and deep learning (transformers, SSMs) using Python, C++, PyTorch, TensorFlow, or JAX. The role involves conducting and implementing open-ended research projects to develop predictive trading models for the firm’s global financial market operations.

ML/AI researchers will use PyTorch or JAX for deep learning work across post-training, generative audio, and multimodal problems. They own research direction end to end—from first idea through training, evaluation, and production deployment.

Design and optimize distributed training infrastructure for frontier AI models, working with GPU parallelism, NCCL, RDMA, FSDP/ZeRO, Ray, Kubernetes, Slurm, PyTorch, JAX, Megatron, Triton, and large-scale data pipelines. This team builds the engineering foundation for RL training loops, distributed systems, and experiment reproducibility.

Thomson Reuters★3.9 (4.3k)· London

You'll lead research scientists and engineers developing foundational machine learning capabilities focused on LLM training, data-centric ML, post-training techniques, and evaluation methods using PyTorch, JAX, and TensorFlow. Thomson Reuters Labs conducts advanced ML/NLP research with access to extensive proprietary data, collaborating with academic institutions while publishing findings at top conferences. You'll manage a global team, hands-on coding and experiments, and translate research into deliverables.

Thomson Reuters★3.9 (4.3k)· London

You'll work with PyTorch, JAX, TensorFlow, and cloud infrastructure (AWS, Azure, GCP) on foundational LLM research including continued pretraining, instruction tuning, reinforcement learning alignment, and post-training techniques for reasoning and agent workflows. Thomson Reuters Labs' dedicated ML research division focuses on advancing LLM capabilities through data-centric approaches and novel evaluation methods, collaborating with academic partners and subject matter experts. You'll manage a diverse global team of ML/NLP specialists and engineers conducting cutting-edge research on agent-based AI systems.

Thomson Reuters★3.9 (4.3k)· London

Thomson Reuters Labs seeks a Research Scientist to advance LLM agent research using PyTorch, JAX, or TensorFlow, focusing on agent-based AI systems, post-training techniques, reasoning models, tool use, and evaluation methods. You'll conduct foundational ML research on continued pretraining, instruction tuning, reinforcement learning alignment, and data-centric ML, collaborating with internal teams and academic institutions while publishing findings and developing proprietary models. The role requires a PhD in a relevant discipline and first-author publications at top-tier conferences on agent systems or multi-agent coordination.

Thomson Reuters★3.9 (4.3k)· London

You'll work with large-scale LLMs (>200B parameters) using deep learning frameworks like PyTorch, JAX, and TensorFlow on research spanning LLM training, post-training techniques for reasoning, data-centric ML, and evaluation methods. Thomson Reuters Foundational Research, the company's dedicated ML research division, focuses on advancing AI in high-stakes domains through publishing and internal model development, collaborating with academic partners including Imperial College London. The internship typically runs 4-6 months across London, Toronto, and Zug locations.

You'll develop world models, foundation models, and reinforcement learning systems using Python and PyTorch, focusing on areas like diffusion-based generative modeling, spatial AI, and multimodal learning. Wayve builds embodied AI software and foundation models that enable autonomous vehicles to perceive and navigate complex environments without maps. The role requires 3+ years deploying ML systems in production, a PhD or Master's in ML/Computer Vision/Robotics, and publications at top-tier conferences.

PhysicsX is building an AI-driven simulation software stack for engineering and manufacturing using deep learning techniques including operator learning, geometric deep learning, and generative models. You'll work with Python libraries like PyTorch and JAX to build machine learning models predicting physical system behavior, collaborating with simulation engineers and customers to translate research into production code while publishing and presenting findings across academic and industry venues.

PhysicsX is building AI-driven simulation software for engineering and manufacturing across aerospace, materials, energy, semiconductors, and automotive. You'll work with machine learning, deep learning, and probabilistic methods (PyTorch, JAX, NumPy/SciPy) to develop models predicting physical system behavior, translating physics challenges into mathematical formulations. As Principal Research Scientist, you'll lead research work-streams, set technical direction, plan roadmaps, and mentor junior team members while collaborating with ML engineers, simulation engineers, and customers.

Isomorphic Labs seeks a Research Scientist to develop deep learning models and algorithms for drug discovery using frameworks like JAX, PyTorch, or TensorFlow alongside scientific libraries (NumPy, SciPy, Pandas). You'll design novel ML architectures for computational biology and chemistry problems, analyze experimental results, and implement training/inference systems. Senior candidates will lead ML research projects, mentor scientists, and shape the research roadmap.

You'll work with large language models, ML frameworks like JAX/PyTorch/TensorFlow, and scientific tools including NumPy and SciPy to develop LLM applications for drug discovery. Isomorphic Labs builds AI models for accelerated drug design and molecular modeling, applying frontier AI beyond AlphaFold to unlock faster breakthroughs in medicine. Depending on experience, you may lead ML research projects, provide technical mentorship to the research community, and shape the research roadmap.

Hudson River Trading★4.5 (2)· London

You'll work on deep learning and foundation models that ingest market data to predict future market states, using GPU clusters with support for PyTorch, JAX, C++, and FPGA/ASIC development. Hudson River Trading's HAIL team develops and deploys these models directly for its trading operations, with researchers having significant autonomy within a small, focused team to improve all aspects from data featurization through architecture design to trading decisions.