Senior Researcher - Machine Learning for Life Sciences - Microsoft Research

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Research Sciences

Senior Researcher in Machine Learning for Life Sciences at Microsoft Research, focusing on advancing AI for biomedicine and life sciences discovery. The role involves designing, implementing, and evaluating novel AI methodologies, including post-training, inference-time optimization, interpretability, and experimental design. It also requires application-specific benchmarking, interpretation of deep learning models on biological data, and developing approaches for inference-time optimization.

What you'd actually do

  1. Design, implement, and evaluate novel methodologies for scientific discovery through artificial intelligence, non-exhaustively including techniques around post-training, inference-time optimization, interpretability, and experimental design.
  2. Application-specific benchmarking and interpretation: Invent and apply techniques for developing a deep understanding of the capabilities of deep learning models as they relate to specific biological data domains and life sciences research questions of interest.
  3. System Optimization: Develop approaches for inference-time optimization of interaction patterns with deep learning models, e.g., context optimization, intelligent sampling, etc.
  4. In addition to these specific technical areas, candidates will be required to participate in robust, repeatable team-based technical research and be effective communicators.

Skills

Required

  • Doctorate in relevant field or equivalent experience
  • Experience creating and using generative AI or other ML techniques in the life sciences
  • Experience working with biological data (e.g., genomics, transcriptomics, proteomics, microscopy), applying both advanced methods and standard bioinformatics tools.
  • Proven track record in bioinformatic algorithm development, benchmarking, interpretation, and application.
  • Experience innovating software, systems, or workflows that leverage generative AI-based systems to solve real-world problems in the life sciences.
  • Experience creating robust, repeatable technical research artifacts as part of an interdisciplinary team.
  • Experience publishing academic papers as a lead author or essential contributor.

Nice to have

  • Experience creating and using generative AI or other ML techniques in the life sciences
  • Experience working with biological data (e.g., genomics, transcriptomics, proteomics, microscopy), applying both advanced methods and standard bioinformatics tools.
  • Proven track record in bioinformatic algorithm development, benchmarking, interpretation, and application.
  • Experience innovating software, systems, or workflows that leverage generative AI-based systems to solve real-world problems in the life sciences.
  • Experience creating robust, repeatable technical research artifacts as part of an interdisciplinary team.
  • Experience publishing academic papers as a lead author or essential contributor.

What the JD emphasized

  • Doctorate in relevant field
  • Experience publishing academic papers as a lead author or essential contributor

Other signals

  • novel methodologies for scientific discovery through artificial intelligence
  • application-specific benchmarking and interpretation
  • inference-time optimization