You will work with Python and ML frameworks like PyTorch to conduct original research in interpretability. The team advances the science of how large AI systems work, developing techniques to understand, debug and steer model internals.

You will design and train custom deep-learning models and fine-tune large language models using Python, TensorFlow, or PyTorch. The work involves leading research for Kangal AI, a venture building frontier AI for governments and security clients. You will own the entire model stack and scale a research team as a founding hire.

You will work with Python, TensorFlow or PyTorch, and large language models, building custom deep-learning models and training/fine-tuning LLMs on a proprietary dataset. Kangal AI builds frontier AI for governments, national security, and private corporations. As a founding hire, you will own the model stack and scale the research into a team you lead.

Founding AI Research Scientist role at Blue Wolf Digital: design, train, and fine-tune custom deep-learning models and proprietary LLMs using PyTorch, owning pipelines end-to-end. The company builds frontier AI for governments, national security, and private organizations, and as the founding hire you'll set research strategy with the CEO, then hire and lead the research team.

Canva★4.3 (32)· London

You'll work with Python, PyTorch, vision-language models (Gemma, Qwen-VL, SmolVLM, MiniCPM-V), optimization techniques (quantization, pruning, distillation, hardware-specific compilation, task-specific fine-tuning), and on-device runtimes (Core ML, LiteRT/TFLite, ONNX Runtime, ExecuTorch). The Video Storytelling team builds models and systems behind Canva’s video AI, partnering with the Edge AI group to explore on-device deployment. This internship optimizes a video-capable VLM for on-device intelligent captioning on consumer phones.

You will work with Python and ML frameworks such as PyTorch to conduct research on interpretability, developing techniques to understand, debug, and steer large AI models. Goodfire is a research company advancing the science of how AI systems work, and you will collaborate with a small, mission-driven team of scientists and engineers in London.

Faculty★0.0 (16)· London

Research Scientists execute technical research in AI safety, using Python with PyTorch to study and steer transformer-based LLMs via white-box and black-box interpretability techniques like mechanistic interpretability and steering vectors. They collaborate with senior scientists at Faculty, a company that builds and deploys human-centric AI for over 350 global clients.

Sonar★5.0 (2)· London

You will work with Python, core ML frameworks, and post-training techniques including GRPO, PPO, RLVR, SFT, PEFT, and preference/safety alignment (Rust or SonarQube flagship languages are a plus). You will join a cross-disciplinary team developing advanced products that post-train models to power enterprise agentic coding practices, ensuring code meets enterprise standards.

You will use Python, core ML frameworks, and post-training techniques (GRPO, PPO, RLVR, SFT, PEFT) to develop enterprise-grade coding agents. This cross-disciplinary team combines Sonar’s static analysis with LLM post-training to help customers build agents generating high-quality code meeting enterprise standards.

Sonar★5.0 (2)· London

Sonar seeks a senior researcher with an advanced degree and 4+ years of ML industry experience. The role involves developing enterprise-grade coding agents using Python, ML frameworks, and post-training techniques (e.g., RLVR, SFT) for Sonar’s AI code verification platform. You will work within a cross-disciplinary team to translate prototypes into products.

American Express★4.1 (9.7k)· London

AI Researcher at American Express will develop advanced ML models for structured data (tabular, sequences, graphs) using transformer, reinforcement learning, and causal techniques, with Python, Spark, C/C++, Java, and Google Cloud tools like Vertex AI and BigQuery. The role sits in AI Labs within Credit and Fraud Risk, driving research into production for credit, fraud, and marketing decisions. Leadership scope is not specified.

You will work with Python, C++, LLMs, deep learning frameworks (TensorFlow, PyTorch, JAX), optimisation methods, reinforcement learning and generative models. The team applies AI to semiconductor design and optimisation while conducting fundamental research for internal use and the scientific community.

Principal researcher role using Python, C++, and frameworks like TensorFlow, PyTorch or JAX, with focus on LLMs, optimization, reinforcement learning and generative models. The research team applies AI to semiconductor design and fundamental research for internal and scientific community. Leadership duties include shaping research strategy and mentoring senior researchers; no team size specified

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 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.

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.

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.

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.