Lead a team of Data Scientists and ML Engineers building AI solutions for financial markets using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, Python, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, Azure AI Foundry, AWS AI Services, and MLOps/LLMOps practices.

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.

Google★4.3 (6.2k)· London

Manage engineers building Google’s Learning Platforms within the Global Business Organization, which powers Google’s ecosystem of products and services for customers and partners. Work with back-end languages (Java, Python, Golang, C++) and front-end (JavaScript, TypeScript, HTML, CSS), plus ML model fine-tuning and evaluation.

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 Python, LLM agents, inference/serving, reinforcement learning, multi-agent orchestration, search/retrieval pipelines, and modern AI coding tools. As a DeepMind Research Engineer, you bridge research and scalable systems, collaborating with scientists on machine forecasting to build 0-to-1 infrastructure, optimize LLM architectures and RL pipelines, and engineer evaluation platforms for autonomous reasoning under uncertainty.

This role develops generative AI applications using Java, Python, React or Angular, LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, and AWS, with tools like GitHub Copilot. You will build scalable, AI-powered solutions for Citi’s global finance operations.

Work with Nvidia GB200 72NVL CX8 and AMD MIxxx architectures, coding in C, C++, C#, Java, JavaScript, or Python to benchmark, profile, and debug GPU clusters. You will help build foundational models powering Microsoft’s personalized AI assistant, Copilot, spanning training and inference infrastructure, model architecture, and data curation.

DeepMind★5.0 (2)· London

Work with Python, LLM agents, LLM inference/serving, reinforcement learning for LLMs, multi-agent orchestration, and modern AI coding tools. The Research Engineer builds and scales infrastructure, models, and tools for the Strategic Bets team, transforming research hypotheses into experiments focused on autonomous systems for machine forecasting.

White & Case★4.0 (120)· London

Works with Python, JavaScript, MCP, agentic AI frameworks, LLMs, and cloud platforms (Azure, AWS) to architect and deploy AI tools. As technical lead of the Applied AI Lab, the role oversees design, development, testing, and deployment of AI solutions to support legal practice and firm-wide innovation. Manages and develops a multidisciplinary team of legal engineers.

Work with Python, LLM agents, LLM inference/serving, reinforcement learning, multi-agent orchestration, search/retrieval pipelines, simulation environments, and modern AI coding tools (e.g., AGY, Claude Code, Cursor). Design, build, and scale infrastructure for frontier machine-forecasting systems, optimizing LLM agent architectures and RL pipelines to enable autonomous systems that anticipate future events and reason under uncertainty.

You will work with Python, JavaScript, Model Context Protocol (MCP), agentic AI frameworks, LLMs, and cloud platforms (Azure, AWS). As technical

Work with Python, PyTorch, scipy, scikit-learn, numpy, pandas, MLflow, Docker, Kubernetes, Ray, and GCP to build and scale distributed compute for research workloads. Phaidra creates AI-powered control systems for the industrial sector using reinforcement learning to enable facilities to learn and improve automatically.

Google★4.3 (6.2k)· London

You'll work with Python, machine learning infrastructure tools, and data optimization platforms within Google Cloud's ML, Systems, & Cloud AI organization. The role focuses on scaling data optimization techniques to improve ML model quality and performance, particularly for Gemini model fine-tuning across product teams like Bard, collaborating with research and product teams to build integrated tools and platforms for data-centric AI development.

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 Claude Code, Cursor, Codex and LLMs for prompt engineering, RAG, agents, evaluation and fine-tuning, building full-stack with backend services, APIs, data pipelines and frontend. You will build the client-facing research and sourcing platform for investors and enterprises to find experts, plus internal systems that automate sourcing, screening, matching and managing experts. You will own full slices of product end-to-end from problem definition through deployment.

Citi★3.9 (21k)· London

Build and scale AI-powered financial systems using Java, Spring, React/Angular, Python, FastAPI, Docker, and GitHub Copilot within Citi’s global tech environment. You will design generative AI agents, LLM workflows, and RAG pipelines while working on enterprise microservices, mobile, or front-end development. This role is for an individual contributor on an agile team.

Deloitte★3.9 (14k)· London

You will work with Python, JavaScript, Kafka, Azure, Kubernetes, Terraform, Helm, LangChain, PySpark, Databricks, and ADLS. You will design, develop, and deploy production-grade GenAI and machine learning systems for the GenAI team within Deloitte’s Data & AI organisation.

You'll work with Python on ML infrastructure and data pipelines, applying data optimization techniques (including those from Google Research) to enhance model quality. You'll build integrated tools and platforms that automate data optimization and run structured ablations for users fine-tuning Gemini models across Google Cloud products.

You will develop large language models, reinforcement learning, generative AI, and optimization techniques using Python, PyTorch, TensorFlow, or JAX, with exposure to CUDA and distributed training. The team applies advanced machine learning research to real-world semiconductor design and AI systems.

Microsoft★4.2 (9k)· London

Works with Python, PyTorch and retrieval technologies including ColBERT, SPLADE, dense/sparse hybrid, RAG and LLM-integrated architectures. Builds state-of-the-art retrieval systems for Microsoft 365 Copilot Search, Chat and Agent experiences, serving millions of enterprise users daily.

Python, fine-tuning (SFT, RLHF/RFT), and generative AI models. Builds and deploys end-to-end AI applications on Fireworks’ generative AI infrastructure for scalable LLM inference, including function calling and multimodal models.

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.

You will work with MongoDB, Postgres, Kinesis, Flink, Spark, ElasticSearch, Redis, Trino, Presto, Snowflake, Kubernetes, and public cloud. The Platform Engineering team designs and develops core data storage, streaming, caching, and indexing platforms that power Scale AI’s products for LLMs and generative models.

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.

You'll work on post-training systems using reinforcement learning, reward modeling, and data generation pipelines to transform pre-trained models into aligned agents. Reflection builds open-weight foundational AI models for various stakeholders, requiring deep LLM training expertise, distributed systems knowledge, and experience improving model behavior through data and RL techniques. You'll collaborate across research and infrastructure teams in a small, high-agency startup environment.