Top Tech Jobs & Startup Jobs in London

Build and deploy production AI applications using Claude models within enterprise customer environments. Develop MCP servers, sub-agents, and agent skills; provide deployment support; identify repeatable implementation patterns; and collaborate with Product and Engineering. The role requires customer discovery, technical solution development, strong communication, autonomous execution, and frequent travel to customer sites.

Lead the sales team for EMEA at Anthropic, focusing on partnerships with nonprofits and educational institutions. Drive revenue while building and managing a high-performing team. Navigate complex sales cycles and stakeholder ecosystems to achieve strategic goals.

Leads the Review Tooling engineering team building investigation, enforcement, analytics, privacy-preserving, and sandbox platforms. Defines the product vision and roadmap, scales human review through Claude-assisted automation, partners with policy, operations, legal, privacy, and data science teams, and ensures reliable systems align with data retention commitments. Responsible for hiring, coaching, technical strategy, stakeholder communication, and engineering-wide initiatives.

Build and operate large-scale observability infrastructure, including telemetry pipelines, instrumentation libraries, eBPF tracing, continuous profiling, cross-signal correlation, unified query tools, and AI-assisted diagnostics. Improve reliability, efficiency, detection, and incident resolution across massive GPU, TPU, and Trainium infrastructure while partnering with research, inference, product, and infrastructure teams.

Lead executive AI strategy engagements with major EMEA enterprises. Responsibilities include conducting executive briefings, shaping AI adoption visions, developing strategic positioning, value cases, proposals, success plans, and reusable industry content. Advise leaders on regulatory considerations including the EU AI Act, GDPR, DORA, and data sovereignty, while partnering with Sales and Applied AI to maintain deal momentum and support execution. The role requires frequent travel across EMEA and engagement with C-suite stakeholders in regulated industries.

Serve as a pre-sales technical advisor for enterprise customers, translating business needs into scalable LLM architectures using Claude. Lead technical discovery, develop evaluations, design integrations with enterprise systems, create tailored technical content, and coordinate across Sales, Product, and Engineering to drive deployments and adoption. Provide occasional on-site workshops and maintain expertise in LLM capabilities and implementation patterns.

Build internal full-stack products across backend, frontend, and infrastructure. Work directly with users to identify needs, create reusable platforms and abstractions, make product and prioritization decisions, and independently ship solutions. Continuously iterate based on feedback while empowering less-technical users. The role requires strong systems thinking, comfort with ambiguity, and proficiency in Python, TypeScript, and React.

Leads European data center capacity transactions from RFP and developer sourcing through term-sheet negotiation, LOI, due diligence, and lease execution. Coordinates legal, finance, engineering, network, government relations, and deployment stakeholders; manages timelines, risks, regulatory requirements, and construction-ready infrastructure development. Builds scalable transaction processes and playbooks while partnering on European market, site, and counterparty strategy.

Own the full enterprise sales cycle for Anthropic’s AI API and SaaS solutions across manufacturing organizations in EMEA. Build relationships with executives, IT, procurement, and operational stakeholders; develop manufacturing-specific sales strategies; identify production, engineering, supply chain, and operations use cases; negotiate complex agreements; exceed revenue targets; and provide customer and market feedback to inform product roadmaps and sales methodology.

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.