Applied Scientist II at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

08 May, 26

Salary

215400.0

Posted On

07 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Transformer Architectures, Ad Ranking, Pricing Optimization, Representation Learning, Multi-Task Learning, Calibration, Offline Evaluation, Online Evaluation, Deep Learning, Large-Scale Experimentation, Marketplace Economics, Weak-Label Generation, Attribution Modeling, Causal Inference, PyTorch, TensorFlow

Industry

Software Development

Description

Overview Our team (Signals Modeling) builds the core intelligence that understands and predicts how users interact with ads - from the first impression through clicks, post-click engagement, and downstream business outcomes. We design and train transformer-based models with billions of parameters that power ad ranking, pricing, and optimization across large-scale consumer surfaces. These models go well beyond simple click prediction: they reason over long user histories, rich ad and content representations, and heterogeneous event streams to infer user intent and advertiser value, even when ground truth signals are sparse or partially unobservable. The team owns end-to-end ML systems, including large-scale data and label construction, representation learning, multi-task and proxy objectives, calibration, and rigorous offline and online evaluation. We build sophisticated training pipelines that transform weak signals (e.g., page visits, dwell time, or engagement events) into high-quality learning targets and deploy models that remain robust under delayed conversions and shifting marketplace dynamics. Engineers and scientists on the team work at the intersection of deep learning, large-scale experimentation, and marketplace economics, shipping production-grade models and data pipelines that directly drive revenue and advertiser ROI. This is a hands-on role with real ownership: you’ll help shape next-generation transformer architectures, push the limits of scalable training and serving, and see your models make measurable impact in one of the world’s largest ads ecosystems. 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. Responsibilities Contribute to modeling and data innovations for ad interaction outcome prediction under partial and noisy feedback. Focus on developing estimated conversion models, supporting data-driven attribution and weak-label generation pipelines, and applying robust learning and calibration methods in scenarios where true user outcomes are sparse, delayed, or unobservable. Partner with senior scientists and engineers to design and evaluate multi-task and proxy-signal models, enhance offline and online measurement frameworks, and help translate modeling improvements into production systems that impact ad ranking, bidding, advertiser ROI, and user experience at web scale. Enjoy working on applied machine learning problems end-to-end while growing technical depth within a highly collaborative environment. Qualifications Required Qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. Preferred Qualifications: Bachelor'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 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. Experience working with noisy, weak, or proxy labels. Exposure to conversion, outcome, engagement, or funnel modeling. Familiarity with model calibration, reliability analysis, or uncertainty estimation. Foundational knowledge of causal inference, attribution, or counterfactual evaluation. Experience in ads, recommendation systems, marketplaces, or other large-scale ML-driven products. Exposure to multi-task or auxiliary-task learning systems. Demonstrated ability to contribute effectively within cross-functional teams. Hands-on experience with modern ML models (e.g., deep learning, tree-based models, or linear models) and feature engineering. Good understanding of supervised learning fundamentals; exposure to multi-task learning is a plus. Experience working with large-scale datasets and contributing to modeling pipelines (data preparation, training, validation, and iteration). Familiarity with offline evaluation methodologies and a basic understanding of online experimentation concepts. Proficiency in Python and experience with at least one major ML framework (e.g., PyTorch or TensorFlow). Ability to execute modeling projects with guidance, communicate progress clearly, and incorporate feedback. Solid collaboration skills and a willingness to learn in ambiguous problem spaces. At Microsoft, our mission is to empower every person and organization on the planet to achieve more. We foster a culture of inclusion, growth, and innovation, built on values of respect, integrity, and accountability. If you're passionate about driving meaningful impact, solving complex problems, and contributing to a growing organization, we would love to hear from you. #MicrosoftA Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 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 $131,400 - $215,400 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-pay This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Responsibilities

Contribute to modeling and data innovations for predicting ad interaction outcomes, focusing on developing estimated conversion models and supporting weak-label generation pipelines. Partner with senior staff to design and evaluate multi-task models and enhance measurement frameworks to impact ad ranking, bidding, and user experience at scale.