Microsoft Senior Machine Learning Engineer 

India, Karnataka, Bengaluru 

111355109

Required:

• Min 8 years of relevant e-xperience
• 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)
• 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:

• Preferred training & fine-tuning experience on large data

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

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 representationsSelect 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