Principal Applied Scientist

Responsibilities

Responsibilities:   -

Optimize model performance, scalability, and efficiency 

- Conduct experiments to evaluate model performance, robustness, and generalization 

- Implement customization techniques for various NN based architectures 

- 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 

- Develop and deploy customized LLM solutions for customer scenarios 

- Optimize models using fine-tuning, distillation, and synthetic data generation 

- Mentor and guide team members to foster innovation and technical excellence 

- Build novel data generation solutions to synthesize complex speech scenarios and finetune models.  

- Build data analysis metrics and solutions to understand the model results, identify gaps, and guide solutions.  

- Collaborate with the global Microsoft team, drive innovative solutions for significant customer asks, and deliver sustained large impacts.  

- Mentor and influence peers, sharing expertise and fostering a growth-oriented inclusive team culture.  

- Contribute to patents and publications at top-tier conferences and represent the team’s technical leadership within and outside Microsoft.  

Qualifications

Qualifications  - 10+ years of experience in machine learning, with a strong focus on GenAI and LLMs  - Depth in Data Science, Generative AI and Engineering  - Ph.D. or Master’s in CS, AI, or a related field  -

Hands-on experience with LLM fine-tuning, model compression, and synthetic data generation preferred  -

A strong 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 models (e.g., BERT, GPT, T5, Llama)  -

Familiarity with cloud platforms (e.g., Azure, AWS) and distributed computing  - Solid understanding of statistics, linear algebra, and probability theory is preferred  -

Excellent problem-solving skills and the ability to work independently and collaboratively  -

Proven ability to build, optimize, and scale AI models in production.