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# Staff Software Engineer

- **Company:** Intuit Inc.
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $202,500.0 - $274,000.0
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, Artificial Intelligence, Batch Processing, Big Data, C Sharp (Programming Language), Spreadsheets, Data Architecture, Data Systems, Software Design Documents, Distributed Systems, Java Platform Enterprise Edition (J2EE), Machine Learning, Object-Oriented Software Development, Anaplan, Salesforce.Com, Search Technologies, Software Engineering, Software Technical Review, Working Model 2D, Apex Code, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Agentic-AI, Information Technology, Apache Kafka, Presto, Data Pipelines, Databricks
- **Published:** October 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=53507bb45652f4af

## About the Role

* 8+ years of relevant experience building web, software, or data-intensive applications, with a track record of leading significant technical workstreams at the senior level or above.
 * Hands-on lead-to-cash domain experience: building or integrating CPQ, quoting, contract, order management, billing, or forecasting systems, ideally on or around Salesforce or a comparable CRM.
 * Strong proficiency in at least one object-oriented language (Java/J2EE or C#), and sound design judgment across distributed systems.
 * Hands-on experience building and shipping AI/ML capabilities in production, ideally including LLM-based features (retrieval-augmented generation, prompt and context engineering, or evaluation frameworks).
 * Ability to influence technical decisions within and across teams, and to communicate trade-offs clearly to technical and non-technical audiences.
 * BS/MS in Computer Science or equivalent practical experience.

Bonus Points:

 * Salesforce platform development (Apex, Lightning Web Components, Platform Events) or Salesforce CPQ/Revenue Cloud experience.
 * Experience with quota, sales credit, incentive, or consumption analytics systems (e.g., Anaplan, Callidus integrations).
 * Exposure to agent frameworks, model orchestration, vector search, or AI evaluation tooling.
 * Kafka, lakehouse environments (Spark, Databricks, Presto/Athena/Trino, Iceberg), and real-time plus batch processing.

## Description

We are seeking a Staff Software Engineer to own the design and delivery of core services in our lead-to-cash domain: pipeline and forecasting, quoting (CPQ), contract management, and order automation. This is a hands-on technical leadership role for an engineer who knows how revenue flows through enterprise sales systems, can lead a domain end to end, and wants to build the AI-assisted deal workflows that replace manual spreadsheet-driven processes.

Intuit has embraced a hybrid working model and this role will be based out of our Mountain View office, 3 days per week.

Responsibilities

 * Design, build, and operate the services behind pipeline management, forecasting, quoting, contracts, and order automation, integrated with Salesforce and the real-time plus batch data pipelines that feed them.
 * Stay deeply hands-on, contributing roughly 70 to 80 percent to development across the software lifecycle, with the remaining time on design, technical alignment, and raising engineering quality.
 * Own complex, ambiguous technical problems within the lead-to-cash domain end to end, and drive cross-team dependencies to resolution in partnership with Senior Staff and Principal engineers.
 * Build revenue-critical systems with the correctness, auditability, and reliability bar they demand: idempotent processing, reconciliation, and clear failure handling.
 * Ship AI capabilities to production responsibly: deal intelligence, assisted quoting, and forecasting features with evaluation, observability, and guardrail mechanisms.
 * Write clear design documents, communicate trade-offs to engineers and cross-functional partners, and mentor senior and mid-level engineers through code and design reviews.
 * Stay current with the AI and data landscape and proactively identify opportunities to apply AI within the domain.