You will work on multimodal AI systems spanning speech recognition and dialog, computer vision (3D reconstruction, VQA), and language (RAG, neuro-symbolic reasoning). The team builds human-centric AI tools for policing, focused on crime-investigation and victim support. This is an applied research role exploring interactive AI architectures and data pipelines.

You will work with PyTorch, TensorFlow, Python, and cloud platforms like AWS or Google Cloud. warpSpeed builds an Application AI tool that enhances productivity through task automation, predictive scheduling, and personalised recommendations. As a lead researcher, you will drive ML and NLP initiatives and mentor junior researchers.

You'll work with machine learning, AI, and object-oriented programming on vast market-data datasets. Your time splits between alpha generation in a trading team and the core engineering team, improving day-to-day performance and building ML tools for a cross-asset liquidity provider.

You will design, test and implement machine learning models in Python, working with tabular data on a sports betting analytics and trading platform. As part of the quantitative modelling team, you'll improve the predictive power of models based on historical event data.

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.

You will use large language models, agentic workflows, inference-time reasoning, probabilistic modeling, reinforcement learning, and ML pipelining tools. At Google DeepMind, you will research AI systems for superhuman probabilistic estimation and forecasting, designing structured reasoning and multi-agent workflows for high-stakes decision-making.

You will lead research into robot learning, world models, and VLA systems, working with real robotic hardware and modern deep learning techniques. You will define the research strategy for a Physical AI startup building intelligent machines.

Work with transformers, diffusion models, multimodal learning, and large-scale distributed training to build foundation models for understanding, predicting, and interacting with the physical world. The team advances multimodal and causal AI systems for applications in robotics, healthcare, and autonomous systems.

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.

You will build secure, sandboxed infrastructure in Python for sensitive model evaluations (CBRN, child safety, dangerous-capability domains), using containerization, VM isolation, RBAC, encryption, and audit logging. Reflection is a research lab building open AI models.

You will work with Python, containerization/VM isolation, RBAC, encryption, audit logging, and data pipelines to build secure, sandboxed infrastructure for running sensitive model evaluations in dangerous-capability domains (CBRN, child safety). You will own the evaluation platform for the Safety team at Reflection, an open AI research lab.

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 work with Python, sandboxed execution environments, containers, VMs, RBAC, encryption, audit logging, and data pipelines. As part of the Safety team at Reflection (an AI research lab building open models), you will design secure infrastructure for sensitive model evaluations in domains like CBRN and child safety, building eval-orchestration tooling and controlled data pipelines.

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.

Work with Python, PyTorch, and MLflow to design and implement deep learning models for time-series simulation of public capital markets. Your work builds Synthera AI's proprietary synthetic data engine, which generates realistic market scenarios for financial institutions. As an early researcher, you will help drive product roadmap and model performance.