About this role
The Network Intelligence Science team at Intuit is seeking a Staff Machine Learning Engineer to build the intelligence layer for their expert workforce network. This role involves transforming a manually coordinated system into an intelligent, adaptive network serving over 100 million customers. You will formulate and solve complex problems in demand forecasting, task assignment, scheduling, and capacity planning. Responsibilities: Architect and own end-to-end ML and optimization systems, including data pipelines, training, evaluation, and serving. Establish engineering standards for models and data systems. Design shared ML infrastructure for experimentation, observability, and deployment. Apply statistical, causal, and reinforcement learning methods to optimize real-time network operations. Build digital twin simulations to evaluate business tradeoffs. Mentor engineers and provide technical leadership across cross-functional teams. Qualifications: BS, MS, or PhD in Computer Science, Software Engineering, Operations Research, or related field. Minimum 7 years of experience in ML or software engineering with a strong ML focus, including production system ownership. Expert proficiency in Python, SQL, and ML frameworks like PyTorch. Experience with optimization tools (e.g., OR-Tools, Gurobi) or reinforcement learning. Strong background in operations research and control theory. Experience with cloud platforms (AWS preferred) and containerization (Docker, Kubernetes). Proven track record of technical leadership and influencing engineering practices. Excellent communication skills for stakeholder alignment.