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.

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.

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.

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.

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.

Use Python and its data toolkit for pipelines, feature engineering, and applied statistics on time-series financial data. You will research and design AI/ML-driven strategies that generate alpha in equities, working end-to-end from hypothesis to live trading models.

Faculty★0.0 (16)· London

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

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

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

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.

Work with Python, PyTorch, and large-scale deep learning (transformers, sequence models, representation learning) to build and deploy models. As a researcher on the Options team at Citadel Securities, you turn petabyte-scale data into trading models with direct impact on options markets.

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.

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.