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.

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.

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.

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.

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

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.

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.

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 will design, implement, and evaluate machine learning models for trading applications using Python and Pytorch. You will apply statistical techniques to solve complex problems affecting global equities, futures, and options markets. You will collaborate with traders, researchers, and engineers to deploy research into production trading systems.

Use Python and PyTorch as a Graduate Machine Learning Researcher at IMC, applying advanced machine learning and statistical techniques to develop trading strategies for global equities, futures, and options markets. The role involves designing, implementing, and deploying models in production.

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.

Axon★3.3 (126)· London

Work with LLMs, MLLMs, Computer Vision, and GenAI using Python, C/C++, TensorFlow, PyTorch, or Keras and ROS. As part of Axon's AI team, advance state-of-the-art models for intelligent reasoning and perception of multimodal data, deploying them in cloud, devices, and robotics. Provide technical leadership to junior scientists.

Nebius★0.0· London

You'll work with Python, Go, and/or C++ to build agentic search systems—a machine-optimized retrieval platform where AI agents iteratively plan, query, and reason over web data at scale. You'll design multi-stage retrieval architectures, develop ranking and embedding-based approaches, create evaluation frameworks for agentic workflows, and mentor engineers while owning technical direction across retrieval and ranking systems. This staff/principal role involves shipping production systems under strict latency and reliability constraints while leading research direction for a fast-growing team at Nebius, an AI cloud infrastructure platform.

You'll work primarily in Python designing and executing empirical research on AI scheming—studying reward-seeking behavior, evaluation awareness, and misaligned preferences in large language models through reinforcement learning experiments and novel evaluation techniques. Apollo Research partners with frontier AI labs to develop a foundational science of scheming, including scaling laws and detection methods for deceptively aligned models. The evals team comprises eight researchers, with individual project leadership and coordination from Alex Meinke.

You'll work primarily with Python to develop and run evaluations assessing risks from scheming AI systems, using Inspect as the evaluation framework. Apollo Research partners with frontier labs like OpenAI, Anthropic, and Google DeepMind to test their most capable models pre-deployment, analyzing model transcripts for behavioral patterns and building novel test environments. You'll join an 8-person evaluations team while collaborating with software engineers and governance staff.

You'll design and implement control protocols for Watcher, a monitoring tool for coding agents, using Python, LLM-as-a-judge techniques, fine-tuning, and red-teaming approaches. The team builds empirical safety mechanisms to detect AI agent failure modes in production environments. You'll join a small team with significant ability to shape the tech and product direction.

You'll work with Python, C++, and related languages to develop world models, 3D/4D scene representations, and robotic planning systems at Reka, an AI research lab building foundational intelligence for physical world applications including robotics, wearables, and media. The role involves collaborating with ML researchers on multimodal generative models and hardware teams to ensure sim-to-real performance across robotic platforms.