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.

Lead 6-7 engineers on core ML infrastructure for a systematic quant fund: distributed model training, LLM hosting/fine-tuning, and scalable deployment. Use Python with working knowledge of C++/Java. Own technical direction for real-world, high-stakes systems.

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.

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.

AXIS Capital★5.0 (1)· London

You will work with Python, Azure AI services (Azure OpenAI, Azure AI Search), Git, LangChain, Semantic Kernel, and MLOps tools for model versioning, deployment, and monitoring. As deputy to the Head of Data Science & AI Delivery, you lead the day-to-day execution of AI delivery across strategic projects and use cases for AXIS Capital, a specialty insurance and reinsurance provider. You directly manage and contribute hands-on to technical design, code reviews, and solution delivery for a growing team of Data Scientists and AI Engineers.

You'll lead a 4–5 person team building machine learning systems using Python, TensorFlow, PyTorch, JAX, AWS, Docker, and Kubernetes for Super's Playstack platform, which powers sports betting and gaming experiences across Europe and Latin America. The role combines technical contribution, project management, and people leadership, focusing on predictive analytics, personalization, fraud detection, and real-time recommendations that enhance customer engagement in online gambling and sports betting.

You will work with TFX, Kubeflow Pipelines SDK, TensorFlow Serving, Triton, TorchServe, TensorFlow, PyTorch, and Python on high-volume data processing and streaming architectures. As part of Fractal, a strategic AI partner to Fortune 500 companies, you will design, train, and deploy ML models for user personalisation, including recommendation engines and ranking algorithms.

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.

bp★3.8 (5.2k)· London

You will work with Python, Java, Azure, LLMs, vector DBs, and RAG patterns, using tools like LangChain, Haystack, and Hugging Face. As part of bp's AI organization, you will design and deploy scalable, production-grade AI applications such as conversational bots and AI-powered search for the oil and energy sector.

You will work with Python, Java, Azure, LLMs, vector DBs, RAG, and frameworks like LangChain, Haystack, Hugging Face, and Crew AI. The AI organization builds and deploys scalable, production-grade AI-powered applications for business value within the oil and energy/HR technology sector.

bp★3.8 (5.2k)· London

Design, develop and maintain production-grade AI applications using Python, Java, microservices, AWS/Azure/GCP, CI/CD, LLMs, vector DBs, RAG, and tools like LangChain, Haystack, Hugging Face, Weaviate, and LlamaIndex. The team builds scalable AI-powered applications—including conversational bots, AI search, unstructured data processing, and generative BI—to solve high-priority business problems.

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.

Fitch Ratings★3.8 (136)· London

You'll build ML/AI solutions using Python, FastAPI, PyTorch/TensorFlow, LangChain, LangGraph, and React/TypeScript within cloud-based microservices architectures. The agile team delivers software products for Fitch's BMI business, which provides data-driven market insights and risk analysis to customers globally. You'll design full-stack enterprise applications, implement GenAI features with LLM integration, and establish development best practices across the product suite.

FDM Group★2.9 (270)· London

The role involves Python, Java, microservices, CI/CD, cloud (AWS/Azure/GCP), LLMs, vector DBs, RAG, AI agents, and frameworks like LangChain, Haystack, Hugging Face, Weaviate, LlamaIndex, Crew AI, AutoGen. The team builds scalable AI-powered applications (conversational bots, AI search, unstructured data processing, GenBI) for a client in the energy sector.

Research, design, and build production-grade ML models using Python, PyTorch, TensorFlow, or JAX, with deployment on cloud infrastructure (AWS, GCP, Azure) and on-device inference on clinical monitoring hardware. The work supports Circadia’s platform, which uses radar to capture contactless physiologic data paired with EHR-integrated clinical context, advancing novel areas like physiological foundation models and bed-exit detection. The role reports to a Principal ML Engineer.

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.

Design, implement, and deploy machine learning models; develop and optimize LLM solutions; work with Python, TensorFlow, PyTorch. This intern will program and fine-tune AI tools for the IT teams at Medpace, a full-service clinical CRO that provides Phase I-IV drug development services.

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.

You will work with Python, PyTorch, TensorFlow, MLOps, CI/CD, and cloud-based AI systems. You will lead the technical strategy for production ML systems that transform raw sports data and live video into real-time insights and personalised fan experiences. You will mentor engineers and data scientists while owning technical direction and delivery.

You will use Python, PyTorch, TensorFlow, Hugging Face, and orchestration frameworks like LangChain to build and deploy ML/AI models. You will also work with Generative AI, Agentic AI, LLMs, RAG, and vector databases, deploying solutions on Azure, AWS, or GCP. You will prototype and productionize AI innovations for Global Payments, a major payments company.