採用
Overview Follow-on Suggestions are prompts that appear after an AI assistant or search engine, such as Copilot or Bing, responds to a user query. These suggestions, including features like "People Also Ask" and "Related Searches" in Bing, help users continue their search, complete tasks, and explore related topics efficiently. The Follow-on Suggestions team within STCI, part of the Microsoft AI (MAI) organization, is responsible for developing these features for Copilot and Bing.
The team is seeking a motivated and self-driven Applied Scientist II with a passion for working on machine learning and deep learning models at internet scale. The ideal candidate will possess strong ML/DL skills, practical experience with real-world problems, and expertise in enhancing products through advanced machine learning techniques. Familiarity with one or more ML/DL frameworks, a deep understanding of algorithms, and hands-on solution implementation are crucial. The position requires excellence in scientific thinking and execution, efficient experiment definition, development of state-of-the-art algorithms, and broad NLP engineering problem-solving abilities. We value individuals motivated to tackle challenging issues, eager to work within a large team of data scientists and engineers, and ready to make an impact on how the web operates.
Our team comprises engineers, scientists, and program managers from diverse backgrounds and experiences, united by a shared vision to make the world smarter and more productive. We believe that inclusive teams, focused on customer needs and mutual trust, are essential for creating outstanding products. Collaboration across the company enables us to leverage technological breakthroughs and integrate AI into various Microsoft products. For a glimpse into our team culture, visit our Instagram page (@lifeatstci). We encourage you to learn more about this role and apply!
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.
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Responsibilities- Develop novel machine learning and data mining algorithms, and fine-tune LLM/SLMs to support follow-on suggestion experiences in Copilot and Bing.
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Create reusable modules with data quality checks for model training and evaluation.
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Host ML models and manage model implementation using production-ready, optimized code, evaluating trade-offs between cost, performance, latency, and throughput to determine optimal operating points.
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Design and execute offline and online experiments to assess product impact, iterating on techniques to improve models and maximize effect.
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Collaborate with design, software engineering, and platform teams to integrate LLM/SLMs into engineering systems for follow-on scenarios.
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Promote engineering excellence by developing secure-by-design, privacy-preserving solutions.
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Maintain live-site reliability and operational excellence, participate in On-Call rotations, and foster a sustainable, high-ownership engineering culture.
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Qualifications- Master’s or Ph.D. degree in Computer Science, Electrical Engineering, or related fields with at least 2 years of experience in Machine Learning/NLP and data mining, or a bachelor’s degree with 4+ years of experience in these areas.
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Strong research and/or development background in architecting, developing, and delivering advanced machine learning, deep learning NLP projects is preferred.
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Minimum 2 years of hands-on experience with ML frameworks such as Py Torch, Tensor Flow, Hugging Face, and ONNX.
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Robust data analysis and data mining skills using Big Data frameworks like Spark and Hadoop/Map Reduce.
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Excellent problem-solving, communication, and collaboration skills.
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Customer-focused, strategic, results-driven, self-motivated, and enthusiastic about working in a diverse team and collaborating across geographies.
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Proven track record of successful delivery of large-scale applied ML projects in an industry setting.
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#MicrosoftAI
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.
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Microsoftについて

Microsoft
PublicMicrosoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.
10,001+
従業員数
Redmond
本社所在地
$3000B
企業価値
レビュー
3.8
5件のレビュー
ワークライフバランス
4.1
報酬
4.3
企業文化
3.4
キャリア
3.2
経営陣
3.0
65%
友人に勧める
良い点
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
改善点
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
給与レンジ
5,620件のデータ
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Mid/L4 · Applied Science
1件のレポート
$234,166
年収総額
基本給
$180,128
ストック
-
ボーナス
-
$234,166
$234,166
面接体験
1件の面接
難易度
4.0
/ 5
期間
14-28週間
体験
ポジティブ 0%
普通 0%
ネガティブ 100%
面接プロセス
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
よくある質問
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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