Job description

About the role We are seeking a Distinguished Engineer to shape the technical and scientific direction of Intuit Intelligence. You will combine deep expertise in AI science and machine learning with hands-on experience building agents that solve meaningful customer problems. You will connect research, model development, agent engineering, and production delivery across multiple organizations. Your work will establish reusable foundations for intelligent products and demonstrate where emerging AI capabilities can create durable business value. At least 40% of your time will be devoted to technical contributions, including experiments, prototypes, agent development, evaluation, and production problem-solving. Responsibilities What you’ll do • Define the long-term AI and ML strategy, connecting scientific advances to customer needs, product opportunities, and production constraints. • Architect and build agents that reason over context, retrieve information, use tools, coordinate workflows, and complete tasks with appropriate human oversight. • Establish reusable approaches for agent orchestration, context and memory management, capability integration, and recovery from failures. • Guide model selection, adaptation, training, and inference decisions using scientific evidence and explicit tradeoffs in quality, latency, and cost. • Build evaluation systems that measure model quality, end-to-end task completion, robustness, safety, and customer value. • Establish production practices for monitoring, experimentation, model and agent lifecycle management, and continuous improvement. • Address difficult challenges in grounding, uncertainty, generalization, permissions, privacy, and reliable execution. • Develop firsthand judgment through research and implementation, advancing promising ideas from experiments into validated product capabilities. • Partner with engineering, product, design, data science, and security leaders; mentor senior technical talent and model responsible agent-driven development. Qualifications What you’ll bring • Deep expertise in AI science and ML, including statistical learning, deep learning, experimental design, and rigorous evaluation. • Demonstrated experience building and operating production AI systems and agents that perform consequential workflows. • Strong understanding of foundation models, retrieval, tool use, agent orchestration, and techniques for adapting models to domain needs. • The ability to distinguish improvements in model behavior from improvements in system reliability and customer outcomes. • A record of translating research into scalable products and reusable technical capabilities. • Strong software and systems engineering judgment, with the ability to influence technical direction across organizations. • The ability to explain scientific uncertainty and technical tradeoffs clearly to executive and technical audiences. What success looks like AI and agents complete customer tasks with measurable quality and reliability. Scientific advances translate into adopted product capabilities, reusable foundations accelerate delivery, and teams improve customer outcomes while managing operational cost and risk. 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 (see more about our compensation and benefits at Intuit®: Careers | Benefits ). 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: Mountain View $316,000 - $427,000

Requirements

Must-haves

What you’ll do
Define the long-term AI and ML strategy, connecting scientific advances to customer needs, product opportunities, and production constraints.
Architect and build agents that reason over context, retrieve information, use tools, coordinate workflows, and complete tasks with appropriate human oversight.
Establish reusable approaches for agent orchestration, context and memory management, capability integration, and recovery from failures.
Guide model selection, adaptation, training, and inference decisions using scientific evidence and explicit tradeoffs in quality, latency, and cost.
Build evaluation systems that measure model quality, end-to-end task completion, robustness, safety, and customer value.
Establish production practices for monitoring, experimentation, model and agent lifecycle management, and continuous improvement.
Address difficult challenges in grounding, uncertainty, generalization, permissions, privacy, and reliable execution.
Develop firsthand judgment through research and implementation, advancing promising ideas from experiments into validated product capabilities.
Partner with engineering, product, design, data science, and security leaders; mentor senior technical talent and model responsible agent-driven development.
What you’ll bring
Deep expertise in AI science and ML, including statistical learning, deep learning, experimental design, and rigorous evaluation.
Demonstrated experience building and operating production AI systems and agents that perform consequential workflows.
Strong understanding of foundation models, retrieval, tool use, agent orchestration, and techniques for adapting models to domain needs.
The ability to distinguish improvements in model behavior from improvements in system reliability and customer outcomes.
A record of translating research into scalable products and reusable technical capabilities.
Strong software and systems engineering judgment, with the ability to influence technical direction across organizations.
The ability to explain scientific uncertainty and technical tradeoffs clearly to executive and technical audiences.
What success looks like
Mountain View $316,000 - $427,000

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningml

Platform: career-feeds
Effort: medium