Machine Learning Engineer Jobs with Visa Sponsorship in London

Work with React, TypeScript, Python/Node.js, Next.js, NoSQL/Relational databases, Docker, and cloud deployments. You will collaborate with public sector clients to build full-stack AI applications and custom LLM training data. Scale’s Global Public Sector team applies AI to critical government challenges.

Anthropic★5.0 (1)· London

You will work with Q-Sys, Google Meet, Logitech, Lua scripting, Netgear AV-over-IP switches, and DSP. As an AV Engineer at Anthropic—which builds reliable, interpretable AI systems—you will design and implement AV solutions for new office builds across EMEA to enable frictionless collaboration for AI safety research.

You'll build production data pipelines using Apache Spark (PySpark), Databricks, Delta Lake, and SQL, with Unity Catalog, CI/CD, Terraform, MLflow, Kafka, and cloud platforms like AWS/Azure/GCP. Unifeye is a Databricks-first consultancy delivering Lakehouse data platforms for enterprise clients. No team size is stated.

You will work with Python, Django, FastAPI, PostgreSQL, AWS, and distributed async workers. The team builds an AI platform that processes purchasing records and recipes to measure and reduce carbon emissions for the food industry. You will own the backend for a core product in an eight-person startup.

MUFG★3.3 (1k)· London

Senior AI engineer in MUFG's Global Markets Engineering Office; role builds AI solutions using generative AI, agentic workflows, RAG, prompt orchestration, workflow automation, and data-driven decision-support tools. The position identifies, designs, and embeds AI solutions to improve productivity, control effectiveness, and operational resilience within a specific business product line.

Databricks★3.0 (21)· London

Lead a team of pre-sales Solutions Architects using the Databricks Data + AI Platform, Big Data, AI, and cloud technologies. The team drives adoption of the platform in the UK&I Retail and Consumer Packaged Goods sectors, supporting complex sales cycles and partner go-to-market strategies.

Amazon★3.5 (56k)· London

You will work with Java, C++, or C# and build large-scale distributed systems as part of Amazon's IAM group, which protects customer and corporate data. This is a greenfield initiative focused on security innovation through data exploration and machine learning. The role requires cross-functional collaboration across multiple teams and technologies.

You'll develop and refine LLM agents, designing scaffolding, running research experiments, and building internal benchmarks to improve reasoning and probability distributions. Mantic is a London-based AI startup that builds systems for judgmental forecasting of geopolitical and economic events.

Design, build and operate production agentic AI systems using Python, LLMs (Azure OpenAI, Anthropic, open models) and frameworks like LangGraph, LangChain, LlamaIndex, as well as MCP servers, vector databases, RAG, and observability tools. The team delivers reliable, audit-ready AI for banking, insurance and fintech clients at a global IT solutions provider.

Kaluza★5.0 (1)· London

In this pre-sales engineering role, you'll build mock backends and demo infrastructure using Node.js, TypeScript, React, REST/GraphQL APIs, AWS, PostgreSQL, DynamoDB, and Terraform, leveraging LLMs like Claude and Gemini for rapid prototyping. You'll support Kaluza's Energy Intelligence Platform, which uses data and AI to help energy companies transition to clean energy, by creating technical assets for client validation. The role involves mentoring the pre-sales team on API design and GenAI workflows.

Anthropic★5.0 (1)· London

Support macOS, ChromeOS, iOS, Android, Google Workspace, Slack, GitHub, Atlassian, and write Bash/Python scripts for automation. This IT Operations role at Anthropic, an AI safety company, handles employee hardware/software support, onboarding, and process improvements.

Use vulnerability scanning tools and CIS/NIST baselines to harden cloud services, while applying AI Development Risk Frameworks to LLM features and MCP integrations. You’ll join the Security Engineering & Operations team protecting patient and NHS data across accu

Work with Python, modern AI platforms, LLMs, APIs, distributed systems, MCPs, and tool-use frameworks to build AI-powered applications, assistants, and agentic workflows. You will drive AI adoption across QRT’s High Frequency Trading business, creating tools that improve productivity in research, development, and trading operations.

You will work with LLM integration, prompt engineering, evaluation pipelines, APIs, data plumbing, and AI-assisted tools like Claude Code, Cursor, or Codex. weframe builds custom AI/tech systems for business owners, embedding directly with client teams to scope and build AI-powered solutions. You'll report to the technical co-founder on a small, hands-on team.

You will design and implement data pipelines and ETL processes using Microsoft Azure tools (Data Factory, Azure Synapse, Azure Databricks), PostgreSQL, T-SQL, and Python with Pandas and PySpark. Your team builds cutting-edge Agentic solutions for internal and global customer use, curating datasets and maintaining the data lake.

You'll work with NVIDIA's AI stack: TensorRT-LLM, Triton, RAPIDS, NIM, NeMo, AgentIQ, and Azure AI services, plus Python, Docker, and Kubernetes. You'll design, prototype, and deliver secure, scalable NVIDIA-enabled AI proofs of concept, accelerators, and production solutions for enterprise and public-sector clients.

You'll work with TypeScript, React, and Python, integrating LLMs, AI agents, or AI-powered APIs. This AI startup automates workflows for complex industries, combining AI, automation, and intuitive user experiences to transform manual processes.

You’ll work with Go, Python or C++, React, TypeScript, AWS, Kubernetes, and AI/computer vision, shipping end-to-end features for a fast-growing AI company building intelligent systems with multimodal AI and real-time data. The small, highly technical engineering team handles backend, frontend, and AI-powered customer products.

Wise★3.5 (42)· London

You’ll build and maintain label integrity for Risk ML models using Python or Java, SQL, ML frameworks (neural networks, NLP, Transformers/LSTMs, GNNs, SVM), data pipelines, and Kafka. Joining Wise’s Risk ML and Intelligence team, you’ll own label quality, statistical monitoring, audit design, and end-to-end model training/deployment at the global money-movement company.

As Technical Lead, you will tackle complex backend and AI challenges, feeding context to AI models and designing systems for unstructured data. You will own a product domain end-to-end within an AI-native data collection platform for physical inspection. You will also coach engineers and align multiple team members around a technical vision.

You will work on world models, diffusion models, and multimodal AI, developing novel architectures, training paradigms, and large-scale distributed training/inference infrastructure. The lab creates next-generation foundation models to understand, predict, and interact with the physical world, targeting applications in robotics, autonomous systems, and other domains.

Work with world models, diffusion models, transformers, video generation, multimodal learning, and large-scale ML systems. As part of Odyssey's applied research team, you'll advance causal, multimodal world models for robotics, science, and other fields, building from hypothesis through working prototypes and shipping results into production models.

Build GenAI applications with LLMs and orchestration frameworks using production-grade Python on Azure and Databricks. Apply CI/CD, containerisation, and MLOps to take ML systems from ideation to production for a UK consumer services company’s 15-person central AI function.

Anthropic★5.0 (1)· London

You'll work with Python, Rust, Go, or Java on Kubernetes clusters, AWS/GCP, and infrastructure-as-code. The Infrastructure team builds and operates distributed systems that train, serve, and secure Anthropic’s AI models—including data pipelines, databases, and developer tooling.

You will work with a modern typed stack across backend, data, and interface layers, build with large language models for retrieval, extraction, and evaluation, and produce browser automation for legacy systems. You own delivery end-to-end for enterprise clients, extracting shared platform infrastructure like RegPulse from consulting engagements. This is the first permanent engineering hire; you set the technical standard and build the team that follows.