The Senior Machine Learning Engineer will join the Growth Intelligence team at Microsoft AI, focusing on enhancing Copilot, the personal AI assistant. This role involves designing and building machine learning models and pipelines for user and conversation understanding. The engineer will work throughout the model lifecycle, from data preparation to production deployment. The position emphasizes collaboration with various teams to translate research insights into practical features.

• Bachelor's Degree in a relevant field and 4+ years of related experience, or a Master's Degree with 3+ years, or a Doctorate with 1+ year of experience.
• Proven experience in natural language processing (NLP) and modern transformer architectures.
• Experience with text embedding models, vector databases, and retrieval-augmented generation patterns.
• Familiarity with distributed training, model optimization, and serving ML models at scale.
• Proficiency in Python and ML frameworks such as PyTorch or Hugging Face Transformers.
• Experience working with data platforms like Spark, Databricks, or Azure ML.

• Design, train, evaluate, and deploy machine learning models for natural language understanding tasks.
• Architect scalable, production-grade training and inference pipelines using modern ML frameworks.
• Develop and fine-tune transformer-based models and maintain embedding pipelines for semantic search.
• Drive rigorous offline and online experimentation to measure model quality and improve product metrics.
• Collaborate with data engineers, data scientists, and product teams to align model outputs with product goals.
• Proactively monitor model performance in production and address scalability and reliability challenges.
• Identify opportunities to improve model architectures and mentor others on ML best practices.

• Employees at Microsoft are often offered comprehensive, “world-class” benefits—including health and mental-wellness programs, competitive pay with bonuses and stock awards, and retirement/savings options. Time-off and flexibility are common, with generous vacation and holidays, parental and caregiver leave, and flexible work schedules, alongside learning support, employee resource groups, product discounts, and matching-gifts/volunteering programs. Specific benefits can vary by region.