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.

Work with large language models, agentic workflows, inference-time reasoning architectures, common scripting languages, ML pipelining tools, probabilistic modeling, reinforcement learning, and possibly prediction markets. As part of DeepMind, you will build AI systems for superhuman probabilistic estimation, tackling forecasting issues like temporal reasoning and multi-step reasoning, and deploy research into high-stakes decision-making environments.

You will work with Python, modern ML frameworks, deep learning, LLMs, RAG, Agentic AI, and Multimodal AI. You will join a newly formed research team at a major UK organisation, exploring next-generation Generative AI and defining technical direction for large-scale customer-facing AI products, turning research into production systems.

Work with PyTorch, SFT, RL, distillation, adapter-based methods, PEFT, preference learning, and large-scale training infrastructure (Ray, Slurm, Kubernetes). You will own the control interface for Luma's video foundation models, focusing on fine-tuning, personalization, and domain adaptation to turn general models into precise tools for creative partners.

Design and develop next-generation Vision Transformers and multimodal LLMs using PyTorch. Work on multimodal alignment, representation learning, and large-scale distributed training for image, video, audio, and text datasets. Join a research organization advancing multimodal AI and embodied intelligence.

DeepMind★5.0 (2)· London

Work with large language models, agentic workflows, inference-time reasoning architectures, common scripting languages, and ML pipelining tools. You will conduct research to build AI systems capable of superhuman probabilistic estimation for high-stakes decision-making, focusing on structured reasoning and multi-agent workflows.

You will work with large language models, agentic workflows, and inference-time reasoning architectures, using scripting languages and ML pipelining tools. The team builds AI systems capable of superhuman probabilistic estimation, focusing on forecasting with no clean time series.

You will work with Python, PyTorch, Kubernetes, Docker, Ray, and GCP to design and evaluate model-based RL agents and world models. Phaidra builds AI-powered control systems for industrial facilities, and you will lead research on algorithmic architecture. You will mentor Research Engineers and own the development and rollout of a research area.

Develop classifiers, data pipelines, automated evaluations, and cross-context monitoring systems using model activations and chains-of-thought. The Safety Oversight team at Google DeepMind uses large-scale production traffic to detect model misbehavior and user misuse. This new team within the GenAI safety organization ensures real-world safety and alignment of deployed Generative AI models.

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.

DeepMind★5.0 (2)· London

Builds classifiers, data pipelines, and automated evaluation methods using parallelised data pipelines, statistical modeling, and model activations/chains-of-thought. The team monitors deployed generative AI models' safety and alignment by analyzing large-scale production traffic and developing cross-context systems to detect coordinated harms and misuse.

Build classifiers and data pipelines to detect model misbehavior and misuse, and develop cross-context monitoring systems for coordinated harms using Gemini and GenMedia models. You will research novel monitoring methods (e.g., model activations, chain-of-thought) and collaborate with infrastructure teams. The Safety Oversight team monitors the safety and alignment of deployed AI models using large-scale production traffic and automated evaluations.

You will build classifiers, data pipelines, and automated evaluation systems to monitor deployed generative AI and LLM models for safety and alignment issues. As part of Google DeepMind's Safety Oversight team, you will detect production safety failures, model misuse, and coordinated harms at scale.

Avanti★3.5 (97)· London

Working with machine learning, cognition, and human behavior, you will build AI systems that deeply understand people by developing the missing layer between users and foundation models. Work focuses on memory, personalisation, user modelling, preference learning, post-training, model evaluation, human-AI interaction, and scalable ML infrastructure.

Work on world models using diffusion and transformer paradigms while designing novel architectures, learning objectives, and training frameworks. This role leads original research into temporal coherence, causal reasoning, and long-horizon prediction at an AI lab building causal, multimodal systems that predict and interact with the world. Mentor a small research group.

You will work with Python, PyTorch, JAX, TensorFlow, and model optimization techniques (RLHF, finetuning). You’ll build models that continuously evolve with the world, centering on efficiency, gradient-free exploration, real-time learning, and interface design.

Faculty★0.0 (16)· London

Python, language models, generative AI architectures, and Bayesian methods are core

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.

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.

Spotify★4.1 (116)· London

Work with Python, PyTorch, NumPy, and diffusion/flow matching models for generative audio. Join Spotify's Artist-First AI Music Lab to research vocal synthesis, post-training alignment, and audio editing, creating new listening experiences for fans and artists. Conduct large-scale experiments using Spotify's infrastructure.

You'll use Python (NumPy, SciPy, JAX, TensorFlow) and distributed training for large-scale diffusion

Meta★4.0 (1.1k)· London

You will work on training pipelines, model evaluation, and benchmark design for large AI models and agents, using formal program verification to improve AI safety and reliability and vice versa. You will join Meta’s AI Verification team, contributing to experiments and publications that connect symbolic verification with neural AI.

Meta★4.0 (1.1k)· London

Work with deep learning frameworks (PyTorch, TensorFlow) and Python to develop algorithms for predictive world modeling, active perception, and robotic interaction. The Surreal Spatial AI group at Meta Reality Labs Research builds machine perception and 3D generative systems enabling robotic agents to perceive and interact with the world. Research spans multimodal generative AI, embodied reasoning, and robotic manipulation.