Staff Machine Learning Engineer

The Network Intelligence Science team builds the intelligence layer that transforms Intuit's expert workforce from a manually coordinated, reactive system into an intelligent, adaptive network that ensures the right experts with the right skills are available exactly when customers need them. We formulate the entire expert network — more than 50,000 experts serving over 100 million customers — as an optimal control problem: demand forecasting predicts future needs, assignment algorithms match tasks to workers under real-time network dynamics, scheduling optimizes expert shifts against projected demand, and capacity planning drives hiring and training decisions. We're replacing today's disconnected, human-bridged tools with a nested hierarchy of forecasting, assignment, scheduling, and supply-planning engines — unlocking scenario planning and durable efficiencies as the platform scales combined services and sales revenue. As a Staff Machine Learning Engineer, you'll be a technical leader across the team's initiatives, owning ambiguous, end-to-end problems that span multiple systems. You'll set technical direction, establish engineering standards that others build on, and shape how the team approaches evaluation, data, infrastructure, and production ML quality.

Architect and own end-to-end ML/optimization systems spanning data pipelines, training, evaluation, and serving, taking on ambiguous problems with significant technical dependencies. Establish technical and engineering standards for models, pipelines, and data systems, including schema validation, data contracts, and production-readiness expectations. Design and drive adoption of shared ML infrastructure for experimentation, evaluation, observability, and rollback, improving quality and development velocity across the team. Apply rigorous statistical and causal methods to quantify uncertainty in demand, assignment, and scheduling decisions, and to inform product and business tradeoffs. Apply operations research, optimization theory, control theory, and reinforcement learning to build and continuously tune the demand, assignment, scheduling, and supply engines that run the expert network in real time. Productionize forecasting, optimization, and simulation systems that plan and adjust expert capacity, schedules, and task assignments across real-time and long-horizon time scales, including the serving and feedback infrastructure that keeps the plan and live operations on the same logic. Build simulation and 'digital twin' capabilities that evaluate business tradeoffs and scenario-plan before deploying changes to the live network. Mentor engineers, provide technical guidance through code and design reviews, and delegate meaningful work to raise the team's collective technical bar. Partner with AI Scientists, product managers, and product engineers to translate ambiguous cross-team requirements into coherent technical plans, while identifying and resolving systemic gaps in tooling, process, and architecture.

BS, MS, or PhD degree in Computer Science, Software Engineering, Operations Research, or a related field, or equivalent practical experience. 7+ years of experience in machine learning engineering or software engineering with a strong ML focus, including sustained ownership of production ML systems at scale. Deep proficiency in Python and SQL, with expert-level fluency in ML frameworks such as PyTorch, and experience with mathematical optimization tooling (e.g., OR-Tools, Gurobi, CVXPY) or reinforcement learning frameworks. Proven experience architecting pipelines across forecasting, training, evaluation, and serving, as well as shared evaluation and observability infrastructure. Strong grounding in operations research, optimization theory, and/or control theory, with sound judgment applying causal inference and reinforcement learning to sequential decision-making problems at scale. Experience with cloud platforms, preferably AWS