Top Python Jobs in London

Design, build, and operate low-latency multi-agent LLM systems. Ensure high-quality real-time communication and optimize prompt programming and retrieval processes.

Design, build and maintain infrastructure tooling for deployment, observability, orchestration, disaster recovery and CI/CD to support scalable, highly available production systems and customer integrations.

Build and maintain production-ready infrastructure tooling and automation for deployment, observability, orchestration, backup/disaster recovery, and CI/CD integrations to support scalable, highly available services.

The Senior Machine Learning Engineer will develop production-ready trading models, design tooling and frameworks, and optimize performance using Python and modern ML libraries.

The Research Engineer will develop innovative generative 3D machine learning methods, train and optimize models, and refine 3D content generation processes, focusing on geometric accuracy and integrating physics-aware capabilities.

Design and implement post-training pipelines for biological foundation models, ensuring alignment with therapeutic contexts and delivering value through rigorous experimentation and collaboration with ML and biology teams.

As a Machine Learning Engineer, you'll optimize and scale ML applications in drug discovery, collaborating with a team to enhance model training and deployment.

Build and maintain low latency trading services using Rust, ensuring efficient order management and performance optimization in a high-frequency trading environment.

Design, build, and maintain production data pipelines and warehouse/lakehouse architecture. Implement version-controlled transformations (dbt), orchestration, IaC, monitoring, and data quality. Enable secure, auditable data access for analysts and product teams, and define long-term platform strategy while partnering across engineering, analytics, and product.

As a Platform Engineer - Support, you'll address IT issues concerning Microsoft Windows, assist end-users, and troubleshoot systems to ensure prompt resolutions.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Design, build, and lead development of AppGlass features; develop AI-first tooling and automation; implement enterprise integrations and deployments across Kubernetes and cloud; and improve reliability, testing, performance, and security for production use.

Develop and maintain SFT and RL post-training pipelines for multi-step coding agents, train and adapt LLMs for agent workflows, build evaluation and simulation environments, design metrics and evaluation frameworks, analyze results to improve models and datasets, and collaborate with research, product, and infra teams to ship models into JetBrains IDEs.

Lead design and evolution of core analytical data models, define analytics engineering standards, build and optimize ETL/ELT pipelines, own the BI/semantic layer (Looker/LookML), drive data quality and governance, partner with cross-functional teams, and mentor analytics engineering across the company to enable reliable, scalable decision-making and experimentation.

Design, train, and deploy large language models from scratch; convert business requirements into technical specs; collect and process pretraining/fine-tuning datasets; run distributed training on large GPU clusters; support and improve production ML subsystems and tooling.

The Senior Backend Developer will drive impact by collaborating with stakeholders, design and develop high-quality software, shape architecture, and lead technical direction while promoting innovation and collaboration within the team.

The GenAI Engineer will design and develop AI agents, enhance existing ML systems, manage cloud infrastructure, collaborate on features, and present to stakeholders.

As a Senior Software Engineer, you will evolve Wayve's simulation platform, focusing on robot emulator fidelity, visual fidelity, or scaling, integrating components and leading technical discussions.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

The Software Engineer will design and implement solutions for FX and Cross-Border Payments, taking ownership of the delivery lifecycle and utilising modern technologies. Responsibilities include collaborating with stakeholders, leading deliveries, and improving systems with innovative solutions.

Maintain and scale DevTools SRE systems: improve user-facing developer workflows, build fault-tolerant self-healing architecture, optimize performance, modify open- and closed-source tools (GitLab, TeamCity), and support users while measuring improvements via metrics.

Design, build, and operate production-grade agentic AI systems on Amazon Bedrock. Implement multi-agent orchestration, MCP servers, RAG pipelines, vector stores, and integrations. Use AWS serverless (Lambda, DynamoDB, S3), instrument observability (CloudWatch/tracing), and collaborate with client teams to deliver scalable AI agent solutions.

The role involves enabling AI governance and engineering efforts by providing practical guidance, developing reusable assets, and ensuring continuous assurance across AI initiatives. The director will drive high-quality delivery, compliance, and responsible AI practices across the organization.

As a Principal Applied Data Scientist, lead the development of scalable AI solutions, focusing on NLP and LLMs, while collaborating with teams and ensuring production standards.