Responsibilities
- Architect and own end-to-end ML and optimization systems spanning data pipelines, training, evaluation, serving, and production operations.
- Establish standards for models, pipelines, data systems, schema validation, data contracts, and production readiness.
- Design shared infrastructure for experimentation, evaluation, observability, and rollback.
- Apply statistical, causal, operations research, optimization, control theory, and reinforcement-learning methods to demand, assignment, scheduling, and supply planning.
- Productionize forecasting, optimization, simulation, capacity planning, scheduling, and task-assignment systems across real-time and long-horizon time scales.
- Build simulation and digital-twin capabilities for scenario planning and business-tradeoff evaluation.
- Mentor engineers, conduct code and design reviews, and raise the team’s technical standards.
- Partner with AI Scientists, product managers, and product engineers to translate cross-team requirements into technical plans and resolve tooling, process, and architecture gaps.
Requirements
- Bachelor’s, master’s, or PhD degree in Computer Science, Software Engineering, Operations Research, or a related field, or equivalent practical experience.
- At least 7 years of machine learning engineering or software engineering experience with a strong ML focus and sustained ownership of production ML systems at scale.
- Deep proficiency in Python and SQL and expert-level fluency with ML frameworks such as PyTorch.
- Experience with mathematical optimization tooling such as OR-Tools, Gurobi, or CVXPY, or with reinforcement-learning frameworks.
- Experience architecting forecasting, training, evaluation, serving, and shared observability infrastructure.
- Strong grounding in operations research, optimization theory, and/or control theory, with experience applying causal inference and reinforcement learning to sequential decision-making at scale.
- Experience with cloud platforms, preferably AWS services, and production-scale Docker and Kubernetes.
- Track record of influencing engineering practices through architecture, reusable tooling, technical reviews, and mentorship.
- Excellent communication skills for aligning technical and non-technical stakeholders across multiple teams.
Benefits
- The position may be eligible for a cash bonus, equity rewards, and benefits under applicable plans and programs.
- Compensation is based on job-related knowledge, skills, experience, and work location, with regular pay-equity comparisons across ethnicity and gender.
Categories
About Intuit
Intuit is a global technology platform that helps our customers and communities overcome their most important financial challenges. Serving millions of customers worldwide with TurboTax, QuickBooks, Credit Karma and Mailchimp, we believe that everyone should have the opportunity to prosper and we work tirelessly to find new, innovative ways to deliver on this belief. We encourage conversations on this page and will not delete comments that follow our terms of use. In order to keep this a safe community, the below posts may be removed: Repeated posts of the same content, spam or posts from fake accounts or profiles, offensive language or material, threats to others in the community, posts deliberately aimed to have a negative effect on the community or conversations.