You will use PyTorch, JAX, and scikit-learn to build ML models in low-data regimes for drug discovery and molecular optimization. You will develop active learning, meta-learning, and uncertainty-aware approaches, designing data acquisition strategies that guide experiments, simulations, and compound selection.

The role involves training next-generation cofolding models using PyTorch, JAX, Boltz, AlphaFold-style methods, equivariant GNNs, contrastive learning, self-supervised representation learning, and DEL datasets. The scientist collaborates with ML researchers, chemists, biophysicists, and data engineers to improve protein-ligand binding prediction and AI-driven drug discovery. Lila Sciences builds an AI-powered operating system for autonomous scientific discovery across medicine, materials, and energy.

This role involves working with Python, PyTorch, JAX, CUDA, Linux, Docker, Kubernetes, Slurm, Ray, Flyte, and Argo. The team builds ML and physics-based infrastructure for drug discovery, including simulation workflows and agent-usable scientific pipelines. Engineers improve code quality, GPU efficiency, and distributed execution while making research tools scalable and maintainable.

Work with TensorFlow or PyTorch, Python, and related computer vision/graphics libraries on neural network architectures (convolutional, recurrent, transformer, diffusion-based). The Visual Computing Group (VCG) researches and develops generative models for game rendering and live video streaming to advance state-of-the-art runtime efficiency, visual quality, and latency.

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.

Work with Python, LLMs, RAG pipelines, vector databases, Hugging Face Transformers, LangChain, PyTorch, and Docker to build production AI systems. Design, develop, and deploy AI-powered solutions such as conversational AI, document intelligence, and backend services for the Global Analytics team.

Lovable★4.0 (43)· London

Uses Go, Rust, TypeScript; AWS, Azure, Google Cloud, Cloudflare; Kubernetes, Terraform, Temporal, OTEL, CI/CD; PostgreSQL, ClickHouse, Firestore, Spanner, BigQuery. Builds the platform for Lovable Enterprise enabling secure, cloud-agnostic deployment, defining architecture for sandboxing, networking, encryption, identity, and mentoring engineers.

You will use TensorFlow or PyTorch, Python, and CV/graphics libraries, plus 3D engines like Unreal or Unity, to develop generative models for graphics and video. As part of the Visual Computing Group, you'll design neural networks and ML for PlayStation's game rendering and live

You'll work with PyTorch, TensorFlow, XGBoost, GPUs, CI/CD and containerisation on the Search & Recommendations team, building production-grade ML systems for personalised recommendations, product ranking and AI-driven styling at ASOS, covering the full lifecycle from experimentation to deployment.

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.

Salesforce★4.2 (1.5k)· London

Success Architect works with Agentforce, Data Cloud, Salesforce Clouds, and data platforms like Snowflake, Databricks, AWS. They drive customer success by optimizing technical architecture for data unification and AI-driven automation, enabling personalized engagement and real-time decision-making.

Python, PyTorch, JAX, LLMs, vLLM, vector databases and modern agent frameworks will be used. The stealth AI startup is building a pre-launch, next-generation AI assistant for everyday tasks, conversations and workflows — a 0-to-1 opportunity on a small engineering team.

Work with Java, GitHub Copilot, Devin.AI, Citi Squad, Stylus Workspaces (Gemini, OpenAI, Claude, Llama), Docker, Kubernetes, and CI/CD. Build high-performance, real-time FX risk management systems for a greenfield platform with a 10+ year strategic commitment. As a VP, lead end-to-end delivery and technical direction.

As a Senior Java Engineer, you will work with Java, Oracle, Hadoop, Elasticsearch, and LLMs (Llama, ChatGPT) to re-architect a tier-one bank's capital markets workflow engine, integrating AI to scale from hundreds to thousands of concurrent users handling massive daily transaction volumes.

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.

Use Python, PyTorch, TensorFlow, scikit-learn, LLMs, NLP, and SQL to build and deploy ML models for trade surveillance. You'll enhance analytics that detect market manipulation by analyzing trader behavior and order execution. Collaborate

Design, build and deploy production Generative AI applications, LLM-powered solutions and agentic frameworks using Python, LangChain, LangGraph, and OpenShift. You will automate enterprise workflows with intelligent agents, working with PostgreSQL and pgvector as part of Citi’s COO-Technology team building scalable AI for global operations. This is an Individual Contributor Staff Engineer role.

Build and maintain scalable data pipelines (BigQuery, Cloud Composer, Airflow, Dataflow, Spark, Flink, dbt) on GCP for AI/ML experiences. Use Docker/k8s, Terraform, SQL, shell scripting, and monitoring tools (Monte Carlo, Looker, NewRelic, Splunk). Collaborate with product engineering, data science, and business teams.

Imperial College London★4.2 (238)· London

Work with PyTorch, GPU kernel programming, Verilog, and NVIDIA's Holoscan Sensor Bridge to integrate FPGA-based AI-ISP accelerators with edge GPU platforms. The project targets low-latency, high-throughput sensor-to-GPU streaming for the "Sense-Decide" pipeline in edge automation.

You will work with Python, PyTorch, TensorFlow, and causal inference tools (DoWhy, EconML, CausalML) to build production AI pipelines, APIs, and microservices. You will design and deploy behavioural, sequential, and causal AI models into Digital Twins, agentic decision engines, and intelligent automation. No team size is specified.

HSBC★4.0 (16k)· London

Work with Python, Docker, Kubernetes, Flask/FastAPI, PyTorch/TensorFlow, SQL Server/Postgres, and CI/CD pipelines to build and deploy production-grade ML and GenAI services. You will join the Growth AI programme within HSBC’s Corporate & Institutional Banking Data & Analytics, developing AI applications to identify business opportunities and mitigate client attrition.

Moody's★3.6 (589)· London

You will work with Python, TypeScript, Java, or Go; cloud platforms (AWS, GCP, Azure); Docker, Kubernetes, PostgreSQL, MongoDB, Redis, vector databases; and build AI applications using LLMs, RAG, prompt engineering, and AI agents. The Innovation team builds next-generation internal and external products powered by large language models, AI agents, machine learning, and natural language processing.

You'll work with frontend (TypeScript, React, Angular, Vue, Next.js, Tailwind), backend (Python, Node.js, Java/Spring, C#/.NET), databases (SQL, NoSQL, vector databases), LLM frameworks, and cloud platforms (Azure, AWS, GCP) including Docker and CI/CD. QuantumBlack, an AI division of McKinsey, builds industry-shaping products and tools across diverse industries by pairing software engineers, AI engineers, and data scientists with client leadership. As a Senior Software Engineer, you'll lead architecture design, mentor teams in engineering excellence and Agile practices, and drive technology decisions on scalable solutions.

You will work with Python, PyTorch, JAX, and TensorFlow to develop optimisation, control, and machine learning methods including reinforcement learning and physics-informed ML. The role is in UCL Computer Science's Multi-Sensory Devices Group, whose research covers wearable sensing, robotics, and biomedical devices.

Build with Python, Go, or Java on the backend, and React/TypeScript on the frontend, using PostgreSQL and non-relational stores, with Docker, Kubernetes, and cloud infrastructure (AWS, GCP, Azure). This role owns full-stack delivery of the B2B product—a campaign, device management, and insights platform for retail and brand partners—along with API design and distributed systems. The team includes engineers, product, and platform groups, working closely with the Head of B2B Product.