Build and iterate on full-stack product surfaces and core infrastructure using modern AI tools, LLM orchestration, and multi-step reasoning systems. You will work directly with founders at a stealth-stage fintech that builds an AI-native observation layer for institutional investors.

Work with BigQuery, Airflow, BigTable, DynamoDB, Python, Scala, Spark, TensorFlow/PyTorch, Docker, Kubernetes, and LLMs building end-to-end ML pipelines. You'll own ML-powered features for the Recommendations Experience team, connecting 200M+ users with personalized music.

The Head of Technology will work hands on with Python and SQL (C#/Java beneficial) to build out the hedge fund's internal platform, covering trading and portfolio data flows, analytics and risk applications, research tooling, and operational systems. This role leads the technology function from the ground up, with scope to build a team as the fund scales.

You will work with Python, PyTorch or JAX, Docker, CI/CD, cloud platforms, and MLOps for model serving. The role builds and deploys scalable infrastructure for training, inference, and evaluation on molecular and protein datasets at an AI-driven protein design company.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

You will design and build AI reasoning systems for physical chip design, using post-training pipelines (fine-tuning, RL, reward modelling) and synthetic data generation for a low-data domain. As the first AI hire at a seed-stage deep-tech startup, you will work directly with co-founders and a chip architect on model architecture, training dynamics, and inference optimisation.

The role involves building real-time systems, high-rate sensor nodes, and an inference stack, using low-latency systems programming and sensor/actuator integration. Adexis is a London-based frontier research lab solving dexterous manipulation for physical general intelligence. This is a founding-hire position; no team size is stated.

Build speech model architectures (ASR/TTS) from scratch, own the full ML lifecycle, and deploy low-latency inference pipelines on Kubernetes and AWS. This is a founding-level role at a London pre-seed startup building proprietary voice AI infrastructure for underserved global markets and languages.

You'll work with Python, LLMs for post-training (RL, fine-tuning, distillation), distributed training, and inference optimisation. This YC-backed AI startup builds frontier systems modelling complex large-scale real-world behaviour. As a Research Engineer, you'll own the path from research to production, working directly with the CEO and Head of Research.

You will work with Python and Jupyter notebooks to build data pipelines and retrieval systems, and use GenAI and fine-tuning to deploy monitored AI systems for a real energy business. You deliver full-stack production systems inside a pod that faces real data quality issues and latency constraints.

You'll work with Python, LLMs, LangChain, PydanticAI, embedding models (sentence-transformers), vector databases (Qdrant, Weaviate, Milvus, pgvector), classical NLP, and agentic frameworks (LangGraph, AutoGen) on Azure. You own the XTA text analytics platform, extracting structured intelligence from unstructured case notes for health, housing, and social care.

You'll work with Python, LLMs, RAG pipelines, agentic frameworks, and integrate with Google Ads, Meta, and CRMs. As the second ML hire, you'll design and deploy production-grade agentic systems that automate complex marketing operations for mid-market brands.

The Principal AI Engineer works with LLMs, RAG, fine-tuning, evals, agentic workflows, and Claude Code. They integrate AI as core infrastructure into the company's SaaS product, owning the technical roadmap across the entire suite. They set technical standards and collaborate with founders and senior leadership.

C++ developers will work on ultra-low-latency exchange protocol encoders and automated trading strategies, using C++ and optionally Python, for a data-driven prop fund’s global automated trading systems. The role involves collaborating with researchers and ops in a small, fast-paced team operating in a real-time environment.

You will work with Python, LLMs, agentic workflows, retrieval-augmented generation (RAG), and internal AI platforms. The Tactical AI Automations team deploys and supports AI tools across PIMCO's Portfolio Management and Analytics groups, including operational support for Lane Assist and POET (portfolio optimization tools).

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.

Architect low-latency inference pipelines for multimodal LLMs, TTS, and Vision models using PyTorch/JAX, TensorRT, and ONNX, with custom CUDA/Triton kernels and C++ wrappers. The role bridges cutting-edge research models to real-time interactive entertainment at a seed-stage London AI game studio building intelligent virtual actors.

You will work with Python, JAX, TensorFlow, large-scale distributed training, and reinforcement learning. You will develop advanced ML systems, simulation workflows, and insight tools for the Waymo Driver, including large generative models, LLMs, and vision-language models for autonomous driving evaluation and onboarding.

Azure ML, AKS, Dagster, CI/CD, Terraform, Prometheus, Grafana, Python, Kubernetes, and vector databases. You will own the infrastructure layer for deploying, monitoring, and maintaining ML models and NLP systems serving local authority clients in health and social care.

The Lead Applied Scientist will work with large language models, agentic AI, Python, pandas, NumPy, scikit-learn, Databricks, MLflow, and Spark. The role is the senior scientific authority on AXIS Capital’s Data Science and AI Delivery team, building AI/ML solutions that turn data into information for specialty insurance and reinsurance decisions.

Work with ViT, MLLM, VLM, Python, PyTorch, and large-scale GPU/cluster training systems. Join a London-based AI research team advancing multimodal large models, vision-language understanding, and embodied intelligence for a global technology company.

You will work with Python, SQL, R, MATLAB, C++, Java, Oracle, Postgres, Snowflake, and Git. The Global Quantitative Research Group designs, implements, and supports margin, stress testing, and risk models for clearing houses across interest rate, equity, credit, and commodity derivatives.

This role leads AI delivery for AXIS Capital, a specialty insurance and reinsurance provider. You will work hands-on with Python, Generative AI (RAG architectures, prompt engineering), Azure AI services, MLOps, Git, and orchestration tools like LangChain. You'll manage projects, review code, and deputize for the Head of Data Science & AI Delivery.

Lead a data science team using Python, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, and IR metrics (NDCG, recall, precision) to build search relevance, ranking, retrieval, and generative AI evaluation systems. The team advances AI-powered discovery across Elsevier’s next-generation platforms, focusing on semantic search, RAG, and online experimentation. This Director role oversees a multidisciplinary team of Senior Data Scientists and Data Analysts.

Work with Azure, Terraform, Azure DevOps, container platforms, and AI agents to build and automate cloud infrastructure. You will design deployment pipelines, observability, and engineering tooling that enable rapid delivery of both AI-powered and traditional cloud-native applications.