The ML Staff Engineer role focuses on defining the architecture and operational excellence of Bain's machine learning ecosystem. You will collaborate with engineering, product, and data teams to build scalable ML and LLM-powered systems. This position is ideal for someone who enjoys solving complex engineering problems and mentoring other engineers while working with modern AI technologies. You will play a critical role in translating business challenges into robust technical solutions.

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience
• 6+ years of experience in software engineering, machine learning engineering, or related technical roles
• Experience designing and operating production-grade ML systems at scale
• Strong proficiency in Python, SQL, and production software development
• Deep understanding of NLP, transformer architectures, retrieval systems, fine-tuning, and structured extraction
• Advanced English proficiency (written and spoken)

• Architect and build scalable machine learning pipelines supporting analytics and insight generation across products
• Lead production deployment of LLM-powered systems using both open-source models and commercial APIs
• Define service level objectives (SLOs) and ensure system reliability and operational efficiency
• Establish robust data contracts and monitoring practices to improve data quality and model performance
• Define lifecycle management for data, models, and prompts while establishing CI/CD standards for ML systems
• Mentor engineers and elevate technical standards across teams

• Employees at Bain are often offered comprehensive health coverage for employees and families, including mental, emotional, and behavioral health resources, with access to counseling, coaching, fitness, and fertility support (varies by location), plus strong financial protection (life, accident, disability; some offices add critical illness), tax-efficient programs, retirement savings, and extras like travel insurance, transportation, and mobile phones. Flexible time off and accommodations include paid holidays, vacation, personal/sick time, part-time/reskilling options, multiple leave paths, and (U.S.) 21 weeks of family leave for all parents; a mentorship-driven culture provides lifecycle career support, training, tuition assistance, and connection to a 19k-member alumni network.