Staff Machine Learning Engineer

Staff Machine Learning Engineer

Intuit's AI Research team develops novel AI/ML solutions that power intelligent experiences across Intuit's ecosystem of products. The team conducts foundational and applied research across areas including natural language processing, large language models, recommendation systems, causal inference, and more, partnering with product and platform teams to translate research breakthroughs into customer value.

We are looking for a Staff Machine Learning Engineer to serve as the engineering backbone of this research team. In this role, you will design, build, and maintain large-scale data and training pipelines as well as experimentation infrastructure, and bring software engineering rigor to the full research lifecycle. You will work alongside AI Research Scientists and AI Scientists, enabling their work through robust engineering, and serving as the team's primary engineering interface when transitioning research innovations into product. This includes leading handoff conversations with product and platform teams, negotiating ownership boundaries, and guiding adoption of the team's innovations.

Responsibilities

• Design, build, and maintain large-scale data acquisition and processing pipelines that power the team's research experiments
• Architect and operate enterprise-scale experimentation infrastructure including experiment tracking, logging, reproducibility tooling, and compute orchestration
• Serve as the team's engineering interface to product and platform organizations: lead handoff and integration conversations, establish clear boundaries of ownership, and guide product teams through adoption of research innovations
• Establish and enforce engineering best practices across the team: git workflows, code review, CI/CD for research code, Jira-based project tracking, and scrums
• Create reusable libraries, tools, and components that accelerate the research development lifecycle and leverage Intuit's technology stack
• Conduct and support experiments to evaluate ML models and algorithms, iterating quickly in close partnership with research scientists
• Mentor team members on engineering best practices and hold the team accountable for code quality, documentation, and architectural soundness
• Take research software through the open sourcing process

Qualifications

• Strong CS fundamentals: data structures and algorithms, distributed systems, system design
• Expert-level software engineering: Git, high-quality production-grade code, containerization, cloud infrastructure
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or equivalent)
• Proficiency in AI assisted coding environments
• Deep understanding of the ML development lifecycle, with expertise in data processing, experimentation, and infrastructure
• Experience designing and operating large-scale data pipelines (Spark, Kafka, or equivalent)
• Strong understanding of modern ML techniques: deep learning, NLP, optimization
• Demonstrated technical leadership at the Staff level; 8+ years of relevant industry experience
• Master's degree in CS, EE, or related field (or equivalent experience)
• Strong communication and interpersonal skills; ability to work effectively in a dynamic, research-oriented team
• Preferred:
• Past experience working in or closely supporting a research organization
• PhD in a relevant field

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is:

Bay Area, California: $197,000 - $266,500

EOE AA M/F/Vet/Disability. Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.