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Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Feb, 26

Salary

0.0

Posted On

21 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Applied Scientist, Foundation Models, Prompt Engineering, RAG, Multi-Agent Architectures, Classical ML, MLOps, AIOps, Generative AI, LLM, Bias Mitigation, Privacy Principles, Data Preparation, ML Models, Scalability, Performance Challenges

Industry

Software Development

Description

Research, implement, and fine-tune state-of-the-art foundation models, leveraging techniques such as prompt engineering, RAG, multi-agent architectures, and classical ML to deliver business impact. Build benchmarks, datasets, and metrics to assess language model performance, addressing relevance, bias, hallucination, and response quality through offline and online experiments. Design rapid AI prototypes, contribute to production deployments, debug code, and support MLOps/AIOps for scalable and reliable AI systems. Convert cutting-edge AI research into production-ready solutions, measure impact via A/B testing and telemetry, and align innovations with strategic business goals. Apply fairness, bias mitigation, and privacy principles throughout the AI lifecycle, proactively addressing ethical and security risks such as XPIA attacks. Partner with product and engineering groups to integrate generative AI solutions, share insights on industry trends, and promote knowledge through documentation and internal forums. Prepare and analyze datasets, develop and evaluate ML models using modern frameworks, and address scalability and performance challenges in large-scale environments. Bachelor's degree in Computer Science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND relevant internship experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. Hands-on experience with generative AI OR LLM/ML algorithms.

Responsibilities

Research, implement, and fine-tune state-of-the-art foundation models to deliver business impact. Design rapid AI prototypes, contribute to production deployments, and support MLOps/AIOps for scalable and reliable AI systems.