# Principal Applied Scientist

**Company**: Microsoft
**Location**: Redmond
**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-52/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_d5b4532b-4cf

## 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.

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.

Responsibilities:

- Lead content-quality understanding at scale, designing and deploying models that assess credibility, usefulness, freshness, safety, and diversity across modalities.

- Reduce misinformation/toxicity error rates through prompt- and model-level innovations, building human-in-the-loop and active-learning pipelines that get better over time.

- Advance the recommendation & ranking stack, architecting and productionizing large-scale DNN/LLM-enhanced recommenders.

- Own evaluation and experimentation, defining offline metrics and online methodologies to confidently attribute impact and guard against regressions.

- Champion safety & trust, partnering with policy and platform teams to encode safety standards and editorial principles into the ML system.

- Scale E2E ML systems, collaborating with engineering on data contracts, feature stores, distributed training/inference, and automated rollout/rollback.

- Mentor & influence, providing technical leadership across problem framing, methodology selection, code quality, and publishing/knowledge-sharing.

- Stay close to users, translating user engagements and behavioral history into model objectives and product bets.

Qualifications:

- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience, or Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience, or Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience.

- Preferred qualifications include Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience, or Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience.

- Publications at top AI/ML conferences, expertise with LLMs, multimodal modeling, and retrieval-augmented recommendation, familiarity with counterfactual learning and multi-objective optimization.

- Experience building content integrity/safety systems, demonstrated ability to lead cross-disciplinary efforts, and familiarity with Microsoft stack.

- 2+ years of experience working with recommender systems/ranking or content-quality/safety models at consumer scale, with clear business impact.

- 2+ years of experience in Python and at least one major deep learning framework, with large-scale data processing and training/inference on distributed systems.

- 2+ years of evaluation & experimentation and ML model development lifecycle.

## Skills

### Required
- Statistics
- Econometrics
- Computer Science
- Electrical or Computer Engineering
- Python
- Deep Learning
- Recommender Systems
- Content Quality
- Safety Models

### Nice to have
- LLMs
- Multimodal Modeling
- Retrieval-Augmented Recommendation
- Counterfactual Learning
- Multi-Objective Optimization
- Microsoft Stack

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Source: [Apply at microsoft.ai](https://microsoft.ai/job/principal-applied-scientist-52/?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)