Top Tech Jobs & Startup Jobs in London

The role involves optimizing training for large language models, debugging complex issues, designing experiments, and maintaining production infrastructure, while managing operational challenges during launches.

Lead a London-based connectivity engineering team building proxy, OAuth, and token-management infrastructure for Anthropic products. Hire, coach, and develop engineers; drive roadmap, cross-org alignment, reliability, incident response, and engineering best practices; collaborate with SF teams and partner product, security, and platform groups.

As a Research Engineer, you'll improve RL training infrastructure, identify bottlenecks, collaborate with teams, and enhance reliability of research runs.

The role involves building and maintaining critical systems for AI inference, optimizing compute efficiency, and supporting research and deployment of AI models.

Lead technical strategy and roadmap for developer productivity infrastructure. Design and build scalable, reliable distributed systems, build environments, package/dependency management, CI/CD and developer tooling to enable fast, reproducible builds and improve engineering workflows across a large monorepo.

As a Staff Software Engineer, you'll work on large-scale distributed systems, optimizing performance for AI inference, and managing complex infrastructure challenges.

As a Research Engineer in Alignment Science, you'll conduct experiments on AI safety, focusing on AI control and alignment stress-testing, while collaborating with cross-functional teams.

The Senior Software Engineer will enhance the reliability of AI systems, implement monitoring frameworks, manage failover systems, and lead incident responses, focusing on serving infrastructures for large language models.

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

Four-month full-time research fellowship mentoring fellows to run empirical RL and LLM projects using external infrastructure. Fellows receive compute funding and a weekly stipend, produce public outputs (e.g., papers), and work on RL environments, algorithms, model fine-tuning, and training/evaluation of large models with mentor support.