Responsibilities

• Driving projects from design through implementation, experimentation and finally shipping to our users.
• This requires deep dive into data to identify gaps, come up with heuristics and possible solutions, using LLMs to create the right model or evaluation prompts, and setup the engineering pipeline or infrastructure to run them.
• Come up with evaluation techniques, datasets, criteria and metrics for model evaluations.
• These are often SOTA models or metrics / datasets.
• Hands on own the fine-tuning, use of language models, including dataset creation, filtering, review, and continuous iteration.
• This requires working in a diverse geographically distributed team environment where collaboration and innovation are valued.
• You will have an opportunity for direct impact on design, functionality, security, performance, scalability, manageability, and supportability of Microsoft products that use our deep learning technology.

Qualifications

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 7+ years related experience (e.g., statistics predictive analytics, research)
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience. 
• Prior experience with data analysis or understanding, looking at data from a large scale systems to identify patterns or create evaluation datasets.
• Experience with common machine learning, deep learning frameworks and concepts, using use of LLMs, prompting.
• Experience in pytorch or tensorflow is a bonus.Ability to communicate technical details clearly across organizational boundaries.