SENIOR APPLIED SCIENTIST
Microsoft
SENIOR APPLIED SCIENTIST
Redmond, Washington, United States
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Overview
Microsoft’s Enterprise customers have a challenge – they need the tools to build agentic experiences over public and private knowledge. The Azure AI Knowledge team is leading the way to deliver the next chapter of RAG (retrieval-augmented generation) that can bring together many sources of data and transform data into knowledge. We are expanding Azure AI Search, Azure’s retrieval platform to meet the demands of complex queries that require reasoning and reflection to deliver high quality results. Our customers span many different industries, corpus sizes and scenarios, and our work includes multiple aspects, such as:
- Developing LLM prompts, agents and query execution workflows, often with tight latency constraints
- Collection, generation and filtering of training and evaluation data
- Building off state of the art techniques from across Microsoft and the industry
You can follow our progression from better search, to RAG, to agentic retrieval:
As a team, we leverage the diverse backgrounds and experiences of passionate engineers, scientists, and program managers to help us realize our mission to empower every person and every organization on the planet to achieve more. We believe great products are built by inclusive teams of customer-obsessed individuals who trust each other and work closely together. We collaborate regularly across the company with teams like Bing and Microsoft Research.
A major aspect of this role is to push our development of knowledge retrieval to the frontier. This requires a capable individual who is well versed in both deep learning and LLM techniques – someone who can solve the hardest challenges where the latest reasoning models are best suited, and create low latency options where speed is critical.
If you are passionate about working on the latest and hottest areas in Artificial Intelligence, Machine Learning and data science, all the while making search better for customers across the world and being part of one of the biggest cloud providers, then this is the team you’re looking for!
Qualifications
Qualifications
Required/Minimum Qualifications
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ 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 3+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
- OR equivalent experience.
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pa
Microsoft posts positions for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Responsibilities
- Leads and defines significant aspects of the teams’ applied science strategy, incorporating state of the art research into techniques shipped to production
- Select, develop and build metrics to use across the full range of search engine components;
- Seek out and incorporate customer feedback into dataset construction, evaluations and technique development;
- Provide technical leadership and contribute to experimentation infrastructure and proofs-of-concept to test out new ideas and concepts;
- Advances the quality of the teams experimental codebase to ensure experiments are efficient and repeatable;
- Nurtures and grow multiple collaborative relationships with program management, engineers, and other functions across products;
- Sets the bar for how the team plans, documents and communicates in a clear and efficient way.