About the job
Intuit is looking for a Staff Software Engineer to build AI/ML systems at the core of our products. You'll design and own shared AI capabilities - model training pipelines, evaluation frameworks, and ML tooling - that power next-generation, AI-driven experiences across personal finance, accounting, and tax. You'll work closely with AI scientists, product, and design, moving from proof-of-concept to production through rapid experimentation and iteration. You'll help build the AI capabilities that every product team depends on - long-term memory systems that let AI experiences retain context and personalize over time, and evaluation frameworks that make model quality and regressions measurable at scale. Besides these core AI capabilities, you'll get to work at the frontier of what AI-native products can be: building agent builder frameworks, embedding AI directly into product experiences, and pushing on cutting-edge agentic AI development.
Responsibilities
Design and own shared AI/ML components, frameworks, and pipelines - spanning model training, evaluation, and fine-tuning - used across multiple product teams
Apply ML fundamentals and LLM techniques (prompting, fine-tuning, RAG) to solve concrete customer problems, then evaluate model performance in production
Build long-term memory and context-retention systems that let AI experiences personalize and stay coherent over time, and evaluation frameworks that make model quality measurable at scale
Prototype and build agent builder frameworks and embedded AI experiences, taking cutting-edge agentic AI concepts from early exploration to product-ready implementations
Drive rapid prototyping and experimentation to move from proof-of-concept to high-accuracy, performant systems
Set best practices for ML tooling and developer workflows; mentor other engineers on AI craft
Stay ahead of emerging GenAI and ML developments and identify where they improve existing products
Collaborate closely with AI scientists, product, and design to navigate ambiguity and ship next-generation AI-driven experiences
Architect and build full-stack, AI-native applications end-to-end, from backend services to production LLM integrations
Set best practices for application architecture; mentor other engineers on software engineering craft
Qualifications
Minimum
BS, MS, or PhD in Computer Science or equivalent practical experience
8+ years building production AI/ML systems
Strong CS fundamentals - data structures, algorithms, system design - plus solid ML fundamentals (classification, regression, clustering, neural networks)
Proficiency in Python and standard ML frameworks (PyTorch, TensorFlow, Pandas/NumPy)
Practical experience with LLMs: prompt engineering, fine-tuning, and frameworks like LangChain
Experience building or operating agentic systems, agent frameworks, or long-term memory/context architectures for AI applications
Familiarity designing evaluation frameworks or offline/online eval pipelines for measuring model and agent quality
Cloud platform experience for ML workloads (AWS, including SageMaker)
Track record of launching AI integrations in production and evaluating their real-world impact
Strong cross-functional collaboration skills, partnering effectively with data scientists, product managers, and engineers across global teams
Proficiency in one or more full-stack languages (JavaScript, Java) and experience designing scalable services - microservices, relational/NoSQL data stores, Kubernetes
Preferred
Prior experience leading an engineering effort