채용

Senior Researcher - Machine Learning for Life Sciences - Microsoft Research
United States, Washington, Redmond
·
On-site
·
Full-time
·
2mo ago
보상
$119,800 - $234,700
복지 및 혜택
•Healthcare
•401(k)
•Equity
필수 스킬
Machine Learning
Artificial Intelligence
Research methodology
Overview:
Health Futures is a mission-focused organization within Microsoft Research, working at the forefront of artificial intelligence, healthcare, and life sciences research. We are a global and diverse team of engineers, scientists, and domain experts who are working on expanding the technological frontier of health and life sciences through deep research. We offer a unique and vibrant environment that features innovative academic research, enterprise software development, and real-world delivery, with close feedback loops and rapid iterations between all three. Our mission is to empower every person on the planet to live a healthier future.
We are looking for a Senior Researcher - Machine Learning for Life Sciences to help us advance the ways artificial intelligence can accelerate and advance discovery in biomedicine and the life sciences. This role is ideal for a candidate with intellectual curiosity who wants to craft a research agenda, articulate it clearly to team members with a diverse set of backgrounds, and execute it as a member of that research team. Successful applicants will bring deep expertise about AI and will be passionate about making new discoveries in health and the life sciences.
At Microsoft, our mission—to empower every person and every organization on the planet to achieve more—guides how we partner with customers to deliver trusted, impactful solutions. With a growth mindset culture, we innovate responsibly and measure success by shared progress—people, teams, and customers. Join us to do meaningful work that changes the world and helps shape what’s next for everyone.
*#Research *
- Responsibilities- Design, implement, and evaluate novel methodologies for scientific discovery through artificial intelligence, non-exhaustively including techniques around post-training, inference-time optimization, interpretability, and experimental design.
- Application-specific benchmarking and interpretation: Invent and apply techniques for developing a deep understanding of the capabilities of deep learning models as they relate to specific biological data domains and life sciences research questions of interest.
- System Optimization: Develop approaches for inference-time optimization of interaction patterns with deep learning models, e.g., context optimization, intelligent sampling, etc.
- In addition to these specific technical areas, candidates will be required to participate in robust, repeatable team-based technical research and be effective communicators.
Qualifications Required Qualifications:
- Doctorate in relevant fieldOR Master's Degree in relevant field AND 3+ years related research experience
- OR Bachelor's Degree in relevant field AND 4+ years related research experience
- OR equivalent experience.
Preferred Qualifications
- Experience creating and using generative AI or other ML techniques in the life sciences.
- Experience working with biological data (e.g., genomics, transcriptomics, proteomics, microscopy), applying both advanced methods and standard bioinformatics tools. Proven track record in bioinformatic algorithm development, benchmarking, interpretation, and application.
- Experience innovating software, systems, or workflows that leverage generative AI-based systems to solve real-world problems in the life sciences. This includes techniques like context engineering, prompt optimization, and optimization of test-time compute.
- Experience creating robust, repeatable technical research artifacts as part of an interdisciplinary team.
- Experience publishing academic papers as a lead author or essential contributor.
Research Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 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.
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Microsoft 소개

Microsoft
PublicMicrosoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.
10,001+
직원 수
Redmond
본사 위치
$3000B
기업 가치
리뷰
3.8
5개 리뷰
워라밸
4.1
보상
4.3
문화
3.4
커리어
3.2
경영진
3.0
65%
친구에게 추천
장점
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
단점
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
연봉 정보
5,620개 데이터
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Mid/L4 · Applied Science
1개 리포트
$234,166
총 연봉
기본급
$180,128
주식
-
보너스
-
$234,166
$234,166
면접 경험
1개 면접
난이도
4.0
/ 5
소요 기간
14-28주
경험
긍정 0%
보통 0%
부정 100%
면접 과정
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
자주 나오는 질문
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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