Applied Scientist II at Microsoft

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

30 Mar, 26

Salary

0.0

Posted On

30 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Natural Language Processing, Multi-Arm Bandit, Optimization, Information Retrieval, Auction Theory, Data Analysis, Feature Engineering, Debugging, TensorFlow, PyTorch, Hadoop, Spark, A/B Testing, Model Optimization

Industry

Software Development

Description

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, 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 4 or more years of experience Solid understanding of probability, statistics, machine learning, data science A/B testing & analysis of ML models, and optimizing models for accuracy Experience with Hadoop, Spark, or other distributed computing systems for large-scale training & prediction with ML models End-to-end system design: data analysis, feature engineering, technique selection & implementation, debugging, and maintenance in production Experience implementing machine learning algorithms or research papers from scratch Experience with TensorFlow/PyTorch 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.

Responsibilities

The role involves designing, implementing, analyzing, and tuning complex algorithms and machine learning systems for ad matching, filtration, and ranking. Collaboration with top machine learning scientists and engineers is essential to deliver direct business impact.