Top Tech Jobs & Startup Jobs in London

Own search quality metrics, analytics architecture, Python-based data pipelines, dashboards, and measurement tooling. Translate product and technical requirements into actionable metrics, investigate anomalies and regressions, improve measurement approaches, and collaborate with product managers, engineers, and stakeholders. The role requires search relevance or ranking analytics experience, statistical knowledge, strong analytical judgment, independent problem-solving, and effective communication.

Lead the global employee relations and performance management strategy, including policies, governance, complex case oversight, investigations, disciplinary and grievance processes, and organizational change consultation. Build effective works council and employee representative relationships, manage employment-law and reputational risks, advise senior leaders, strengthen manager capability, and use case data to identify trends and improve organizational practices.

Own optimization of LLM and VLM inference systems from model artifacts through production deployment. Improve latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost per token. Deploy and extend inference engines, build compression and quantization workflows, implement decoding and KV-cache optimizations, create reproducible benchmark harnesses, diagnose production regressions, and collaborate with kernel, platform, research, infrastructure, product, and customer teams.

Own the product strategy and roadmap for large-scale search infrastructure serving AI agents. Set search quality and release standards, manage depth, latency, freshness, and cost tradeoffs, guide index coverage, and shape retrieval capabilities for agentic workflows. Partner with search engineering, research, evaluation, data, customer success, and customers to identify quality gaps and prioritize improvements. This hands-on senior individual contributor role requires experience building and shipping production search or ranking systems at scale.

Own enterprise sales across the UK and Nordics, managing the full cycle from prospecting and pipeline creation through complex deal closure. Build regional presence through outbound sales, partnerships, events, and networks; engage technical, business, and executive stakeholders; apply MEDDPICC and value-based selling; collaborate with product and engineering; exceed revenue targets and provide accurate forecasts.

Own the vertical search product from market selection and data-provider evaluation through roadmap, customer discovery, specifications, launch, adoption, and revenue. Partner with engineering, data, research, evaluation, customer success, and go-to-market teams to build industry-specific AI search products. Define use-case quality standards, shape ranking and product experiences, and prioritize investments across verticals.

Drive cloud capacity expansion across data centers by analyzing resource consumption, forecasting demand, identifying risks, and translating forecasts into infrastructure requirements. Coordinate procurement, hardware delivery, installation, deployment, and customer availability across supply chain, data center, engineering, product, and business teams. Maintain expansion plans, communicate timelines and dependencies, and proactively resolve cross-functional issues.

Serve as embedded internal controls and risk partner for corporate functions (Tax, Treasury, HR). Lead SOX walkthroughs, risk assessments, control design, remediation, audit coordination, and automation efforts to improve control effectiveness and reporting. Support transformations, system implementations and stakeholder engagement to balance risk mitigation with operational practicality.

Lead and coordinate complex, cross-functional compute hardware and software programs (kernel, virtualization, drivers, firmware, GPUs). Manage dependencies, risks, execution, deployment automation, validation, observability, and customer/vendor interactions to scale large GPU/HPC cloud infrastructure.

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.