Director of Applied Research (IC) - Extreme Retrieval
Microsoft
Skills
- Machine Learning
- Information Retrieval
- Transformer Architecture
- RAG
- LLM
- SLM
- Distributed Systems
- GPU Optimization
- Technical Leadership
- System Architecture
About the role
Apply on the link above. Play the role of a technically strong ML+DS Lead who takes overall responsibility for cutting-edge retrieval models. You will bridge various teams across research and engineering and guide them in overcoming any challenges that might arise in real-world scenarios. Co-lead culture, vision, strategy and execution while identifying new opportunities for impact. Drive talent acquisition, attracting top ML talent from academia, industry and startups. Optimize GPU infrastructure and advocate for resources at internal committees.Oversee engineering for cutting-edge ML codebases, bridging developers and ML researchers to deliver robust, scalable systems. Bachelor's, Master's or PhD in Computer Science, Engineering, or related fields. 10+ years of deep technical experience in machine learning and/or information retrieval; able to train ML models on very large-scale data and apply them to real-world scenarios. 3+ years of experience in technical engineering leadership, guiding multiple teams in delivering complex ML/IR systems. Proven ability to engage with engineering teams, understand architectures and map research to engineering needs. Exceptional communication skills; comfortable with senior executives and external partners. Startup-like agility, independence, and a drive for impact and quality. Ability to influence peers and seniors and guide teams not reporting to you; deliver success as a lead individual contributor rather than as a manager/organizational leader. Detailed knowledge of the transformer architecture, attention and dual/cross encoders including low-level implementation details of training and inference. Experience with large-scale retrieval (RAG, LLM/SLM architectures, distributed systems, and GPU optimization). Proven track record of training ML models and delivering significant product impact. Track record in building strategic partnerships and recruiting ML talent.
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