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Come build the future of AI-powered browsing with the Microsoft Edge team. We are a collaborative group of applied scientists and engineers applying AI/ML to enhance web experiences for hundreds of millions of users. Our team has delivered ML-driven features like intelligent site suggestions and ML-powered autofill improvements in Edge, and we continue to innovate on user engagement. We are looking for an Applied Scientist II (Level 61) who will contribute directly to these efforts.
Job Responsibility:
Develop and integrate AI/ML features: Collaborate with product and engineering teams to identify opportunities for AI-driven improvements in Edge and design features that improve user experience
Design, train, and refine ML models (including deep learning and reinforcement learning approaches) to power new browser experiences
Conduct offline experiments and online A/B tests to generate insights and evaluate model performance and feature impact
Build and maintain end-to-end data pipelines and tools for development and inference (from data collection through model deployment)
Stay up-to-date with the latest research trends in machine learning and AI
Mentor and share knowledge with team members to raise the technical bar
Requirements:
Bachelor’s degree in computer science, statistics, engineering, or a related field (machine learning, data science)
OR a Master’s degree in a related field AND 4+ years of experience
Solid understanding of core machine learning concepts (e.g., predictive modeling, classification, optimization) and hands-on experience developing models from scratch to production
Proficiency with ML frameworks such as PyTorch or TensorFlow is highly desirable
Strong coding skills in Python (pandas, PyTorch/TensorFlow, scikit-learn, etc.)
Experience writing efficient code for data processing and model inference in a production environment
Proven ability to analyze and derive insights from large, complex datasets
Experience with data tools and pipelines (SQL, Spark/Hadoop, or Azure Machine Learning pipelines) for handling “big data” is a plus
Excellent analytical and problem-solving skills with a track record of driving improvements through experimentation and data-driven decision making
Ability to design sound experiments and interpret results correctly (statistical rigor, significance testing, etc.)
Strong communication and teamwork skills
Ability to effectively collaborate in a multidisciplinary team, synthesize complex ideas, and communicate technical results to non-experts in clear, concise ways
Hands-on experience with A/B testing and analysis of user engagement metrics
Ability to work closely with product teams to formulate hypotheses and interpret experiment outcomes to inform product decisions
Nice to have:
Experience with deep learning techniques (e.g. neural networks, transformers) and/or reinforcement learning in an applied setting
Familiarity with modern ML model development, tuning, and evaluation practices at scale
A record of research innovation as evidenced by publications in top AI/ML conferences or filed patents is a strong plus
Familiarity with web/browser technologies or prior experience in consumer-facing product teams
Understanding of how browser features work and how users interact with them can help in designing more effective ML solutions (e.g. knowledge of user behavior patterns in web browsing)