Senior Machine Learning Engineer

Within AI Platform, the Azure ML team enables data scientists and developers to quickly and easily build, train, deploy, manage, and consume machine learning models.

Required Qualification:

• Total 8+ years of relevant experience
• Depth in Data Science, Generative AI and Engineering
• Background in machine learning, deep learning, and natural language processing
• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) to develop production-grade quality product
• Experience with distributed systems design and implementation
• Proficiency in Agile development practices and Continuous Integration/Continuous Deployment (CI/CD)
• Experience with transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion)
• Good understanding of statistics, linear algebra, and probability theory.
• Familiarity with cloud platforms (e.g., Azure, AWS) and distributed computing
• Excellent problem-solving skills and the ability to work independently and collaboratively

Preferred Qualifications: 

• Preferred training & fine-tuning experience on large data

#IDCAIPlatformHiring

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

As a (Senior) Machine Learning Engineer in our team, you will:

• Collaborate with researchers and data scientists to design sophisticated machine learning models
• Implement and fine-tune neural network architectures, including transformer-based models
• Optimize model performance, scalability, and efficiency
• Conduct experiments to evaluate model performance, robustness, and generalization
• Explore novel techniques and approaches to enhance model capabilities
• Stay up to date with the latest advancements in NLP, deep learning, and AI research
• Work with large-scale datasets, preprocess them, and create appropriate data representations
• Select relevant features and ensure data quality for training and evaluation
• Collaborate with cross-functional teams, including researchers, software engineers, and product managers
• Communicate technical findings and insights effectively
• Deploy trained models in production environments
• Monitor model performance, troubleshoot issues, and iterate on improvements