Requirements

• Bachelor's, Masters, or Doctorate Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field, or equivalent experience.
• Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice to Haves

• Hands-on experience developing retrieval or ranking systems at production scale.
• Expertise in semantic retrieval, dense retrieval systems, embedding model training or fine tuning, SPLADE or sparse retrieval methods, hybrid retrieval architectures, ranking systems for search or recommendation, large-scale information retrieval systems.
• Experience developing ML systems in Python and modern ML frameworks such as PyTorch.
• Experience evaluating retrieval quality using offline metrics and/or online experimentation.
• Experience with RAG or agentic AI retrieval systems.
• Publications in top-tier conferences such as SIGIR, RecSys, KDD, WWW, WSDM, ACL, or EMNLP.
• Experience shipping retrieval systems integrated with LLM-based products.
• Familiarity with enterprise search, personalization, and recommendation systems.
• Experience optimizing retrieval latency, scalability, and serving infrastructure.
• Experience with reinforcement learning or retrieval-aware reasoning systems.

What You'll Be Doing

• Design and run experiments, define offline and online evaluation metrics, and develop scalable retrieval pipelines and models for enterprise-scale search systems.
• Focus areas include semantic retrieval, dense retrieval and embedding model fine tuning, lexical retrieval approaches, hybrid retrieval systems, query understanding, multi-stage ranking, retrieval-augmented generation (RAG), personalization, knowledge retrieval for agentic AI, reinforcement learning, and LLM-integrated retrieval architectures.
• Apply best practices in Responsible AI, Privacy-Preserving ML, and scalability for production-grade enterprise systems.
• Partner with Engineering, PM, and Design teams to translate product requirements and research advances into scalable and reliable retrieval infrastructure supporting Copilot Search, Chat, and Agent experiences.
• Collaborate closely with Microsoft Research, Azure AI platform teams, and product organizations to bring cutting-edge retrieval and ranking advances into large-scale production systems.
• Understand user retrieval pain points and enterprise grounding challenges, developing solutions to improve relevance, answer quality, freshness, and personalization.
• Provide technical leadership and mentorship to scientists and engineers working on retrieval, ranking, and recommendation systems.
• Establish and evolve evaluation frameworks and success metrics for retrieval quality, grounding relevance, ranking effectiveness, and downstream Copilot quality metrics.
• Stay updated on latest advances in retrieval and ranking research and publish at top-tier venues.

Perks and Benefits

• Competitive base pay range: £74,700.00 - £122,600.00 per year for Applied Sciences IC4 level.
• Competitive base pay range: £93,500.00 - £161,800.00 per year for Applied Sciences IC5 level.
• Certain roles may be eligible for additional benefits and compensation.
• Inclusive culture that values respect, integrity, accountability, and growth mindset.
• Equal opportunity employment promoting diversity and accommodation for religious or disability needs during the application process.