採用
Required Skills
PyTorch
TensorFlow
Spark
Machine Learning
Overview
What if your job description were simply “make tomorrow better?” Every day at Microsoft, we bring an insatiable curiosity to the workplace, challenging ourselves to reimagine what it is and what it can be. We build on what’s come before to create what’s next. We help shape the future and we empower billions of people around the globe.
We are the computational advertising team in the AI & Research organization at Microsoft. We are looking for candidates with research and applied experience in machine learning related areas. Search advertising is a $100 billion market worldwide. Microsoft's Bing search engine supports over 30% of desktop search in the US, with similarly significant presence in many other countries.
Responsibilities:
We are a team of applied scientists working on machine learning components in the whole sponsored search stack. Our team works on problems related to machine learning, deep learning, natural language processing, multi-arm bandit, optimization, information retrieval, and auction theory, among others. Our work entails building large-scale machine learning systems for ad matching, filtration, ranking, and multi objective optimization, and several other ML-driven business problems. You will design, implement, analyze, tune complex algorithms and ML systems and the supporting infrastructure for operating on large datasets. You will collaborate with top machine learning scientists and engineers in delivering direct business impact. We're looking for sound understanding and insight into productionizing machine learning models in large-scale systems, an ability to pick up new technical areas, as well as a commitment to developing, delivering, and supporting algorithms in production.
Qualifications-
MS/BS in CS/EE, mathematical or machine learning related disciplines, with 10 or more years of experience
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Solid understanding of probability, statistics, machine learning, data science
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A/B testing & analysis of ML models, and optimizing models for accuracy
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Experience with Hadoop, Spark, or other distributed computing systems for large-scale training & prediction with ML models
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End-to-end system design: data analysis, feature engineering, technique selection & implementation, debugging, and maintenance in production
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Experience implementing machine learning algorithms or research papers from scratch
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Experience with Tensor Flow/Py Torch and deep learning models is a plus
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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About Microsoft
Reviews
3.8
5 reviews
Work Life Balance
4.1
Compensation
4.3
Culture
3.4
Career
3.2
Management
3.0
65%
Recommend to a Friend
Pros
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
Cons
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
Salary Ranges
5,571 data points
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Mid/L4 · Data and Applied Scientist
0 reports
$202,099
total / year
Base
$149,342
Stock
$32,252
Bonus
$20,505
$139,572
$301,212
Interview Experience
7 interviews
Difficulty
3.7
/ 5
Duration
14-28 weeks
Offer Rate
14%
Experience
Positive 14%
Neutral 29%
Negative 57%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Technical Interview
5
Onsite/Virtual Interviews
6
Final Round
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
News & Buzz
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