Microsoft Applied Scientist II 

India, Karnataka, Bengaluru 

244689979

Required Qualifications:

• Master's Degree in relevant field AND 2+ year(s) related-research experience
  • OR equivalent experience.
• Experience with large-scale models and frameworks such as PyTorch.
• Proven track record of building, deploying, and optimizing large-scale AI/ML models in real-world applications, especially in NLP, Information Retrieval, or Computer Vision.
• Publications in top-tier conferences like NeurIPS, ICML, CVPR, SIGIR, KDD, ACL, EMNLP, ICLR, WWW, WSDM or similar, demonstrating expertise in advancing the field.

Other Requirements:

Candidates must be able to meet Microsoft, customer and/or government security screening requirements that are required for this role. These requirements include, but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Master'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 Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research)
    
  • OR equivalent experience.
• Experience working with large language models / multi-billion parameter models, focusing on their efficient training and online inference.
• Background in developing or modifying deep learning algorithms/architectures to improve computational and memory efficiency.
• Experience in online advertising, search engines, or recommendation systems.

• Drive AI innovation: Lead the development of cutting-edge models that select and rank ads, predict user interaction, and optimize advertiser outcomes. Leverage and advance Deep Learning,
• Reinforcement Learning, Causal Inference, and other techniques to solve complex problems.
• Optimize at scale: Design, build, and deploy models that operate at web scale, ensuring they are robust, scalable, and high performing in real-world settings. Directly improve user engagement, ad relevance, and advertiser return on.
• Collaborate and innovate: Work closely with worldwide research, engineers, data scientists, and product teams to integrate your solutions into Microsoft Ads systems, driving cross-team collaboration and delivering impactful end-to-end solutions.