# Principal Applied Scientist

**Company**: Microsoft
**Location**: Mountain View
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: $142,800 - $274,800 per year
**Category**: Engineering
**Industry**: Technology
**Ticker**: MSFT
**Wikidata**: https://www.wikidata.org/wiki/Q2283

**Apply**: https://microsoft.ai/job/principal-applied-scientist-53/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_1e6139c2-e3f

## Description

As a Principal Applied Scientist at Microsoft AI, you will lead the science behind Discover's ranking and content-quality stack, combining LLMs, multimodal models, and large-scale recommender systems to drive measurable gains in engagement, satisfaction, and trust. You will set technical direction, mentor a high-caliber science cohort, and partner closely with engineering, PM, UXR, and policy to ship end-to-end outcomes. You will contribute to the development of the next generation of MSN that is adopting the latest generative AI techniques.

Lead content-quality understanding at scale. Design and deploy models that assess credibility, usefulness, freshness, safety, and diversity across modalities; reduce misinformation/toxicity error rates through prompt- and model-level innovations; build human-in-the-loop and active-learning pipelines that get better over time.

Advance the recommendation & ranking stack. Architect and productionize large-scale DNN/LLM-enhanced recommenders (representation learning, sequence modeling, retrieval/ranking, slate optimization), balancing user satisfaction, content quality, and business goals.

Own evaluation and experimentation. Define offline metrics (e.g., NDCG, ERR, calibration) and online methodologies (A/B tests, interleaving, counterfactual & bandit approaches) to confidently attribute impact and guard against regressions.

Champion safety & trust. Partner with policy and platform teams to encode safety standards and editorial principles into the ML system; create red-teaming, adversarial, and safeguard layers for generative and curated experiences.

Scale E2E ML systems. Collaborate with engineering on data contracts, feature stores, distributed training/inference, and automated rollout/rollback; drive architectural investments that increase agility and reliability of Discover's AI platform.

Mentor & influence. Provide technical leadership across problem framing, methodology selection, code quality, and publishing/knowledge-sharing; uplevel peers through design reviews, deep-dives, and principled decision-making.

Stay close to users. Translate user engagements and behavioral history into model objectives and product bets; ensure our AI solutions elevate relevance, transparency, and engagement for real users.

## Skills

### Required
- Statistics
- Econometrics
- Computer Science
- Electrical or Computer Engineering
- Machine Learning
- Deep Learning
- Python
- PyTorch
- TensorFlow
- Large-Scale Data Processing
- Distributed Systems
- Recommender Systems
- Ranking Models
- Content Quality
- Safety Standards
- Editorial Principles
- Red Teaming
- Adversarial Layers
- Safeguard Layers

### Nice to have
- LLMs
- Multimodal Modeling
- Counterfactual Learning
- Multi-Objective Optimization
- Content Integrity
- Safety Systems
- Quality-Aware Ranking
- Cross-Disciplinary Efforts
- PM
- ENG
- UXR
- Editorial/Policy

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