Principal Applied Scientist

Microsoft • Redmond, WA

3d

About The Position

The Signals Team at Microsoft Ads develops advanced machine learning models to predict user responses to advertisements. These predictive signals play a crucial role in enhancing user engagement and maximizing advertiser returns.
As a Principal Applied Scientist on our team, you will design and implement large-scale machine learning models, driving their deployment into production and translating your expertise into tangible revenue impact. This role offers a unique opportunity to enhance your skills in building and deploying machine learning models at scale. You will gain invaluable experience analyzing model behavior within a complex, multi-layered model stack, and understanding the intricate interactions between various algorithms. Additionally, you will stay at the forefront of state-of-the-art approaches in this domain.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Requirements

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ 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 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 3+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• Ability to meet Microsoft, customer and/or government security screening requirements 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.

Nice To Haves

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 5+ years customer-facing, project-delivery experience, professional services, and/or consulting experience.
• Machine Learning Model Expertise: Deep experience designing, training, and optimizing large-scale machine learning models, particularly transformer-based models and large language models, with a strong focus on production deployment and performance.
• LLM Inference & Fine-Tuning: Proven ability to build and optimize high-performance LLM inference pipelines and apply advanced fine-tuning techniques (e.g., SFT, LoRA, RLHF) to adapt foundation models for domain-specific use cases.
• Model Quality & Evaluation: Strong expertise in developing robust evaluation frameworks for ML and LLM systems, including automated metrics, human evaluation, benchmarking, and monitoring for issues such as hallucination, bias, safety, and model regression.

Responsibilities

• Build large scale machine learning models for text and numerical data, deploy models in production.
• Run A/B testing, analyze impact of model metrics and possible model failure modes on business KPIs.
• Conduct data exploration and analysis of new features and model improvements.
• Establish scalable pipelines for data processing and analytics, model training and validation.
• Establish automated processes to monitor system health using agentic workflows.
• Track relevant state-of-the-art approaches in the field, identify and implement applications to improve the modeling stack.
• Mentor less experienced scientists on the team.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal