Forward Deployed Engineer
Intuit · On-site · Full-time · Mountain View, California, United States
Posted (22h ago)
- Employer posted
- First seen here
- Repost check
- Same job ID since we first saw it on Oct 20, 2026. If Intuit reposts it, we keep the original posting date.
- Level
- Mid-level
- Work setup
- On-site
- Tech mentioned
- SQL, Python
- Intuit hiring
- 113 roles posted in the last 30 days
Job description
Intuit Enterprise Suite is how the world's fastest-growing mid-market and enterprise finance teams run their books, close their quarter, and put AI agents to work on real accounting operations — not demos. Forward Deployed Engineers (FDEs) are the team that makes that real: embedded with our customers, shipping production automation, and turning “AI pilot” into “AI in the general ledger.” This isn't a traditional sales engineering role, and it isn't a traditional product engineering role either. FDEs report into Engineering, work shoulder-to-shoulder with Sales and Account Management on the deals and renewals that matter most, and own the last mile between what Core Engineering ships and what a specific customer actually runs. You'll sit at the intersection of engineering, applied AI, and enterprise finance, and you'll be judged on one thing: did the customer's business measurably change because you were in the room. Responsibilities What you'll do Pre-sale: build the proof that wins the deal Get hands-on with prospects before there's a contract: build a working proof-of-value directly in (or against a realistic replica of) the prospect's own data and systems — real integrations and real output, not a scripted demo. Partner with Account Executives from first technical touch: run discovery with the prospect's finance and IT teams to understand their chart of accounts, systems, and process gaps well enough to build against them. Lead the technical evaluation: architecture and security/compliance reviews, integration feasibility, and technical RFP responses with the prospect's IT and controllership teams. Build the technical narrative and ROI case alongside Sales, and present it directly to economic buyers — CFOs and Controllers — as part of the deal cycle. Your proof-of-value is very often what gets the deal signed. Flag technical or data-readiness risk early, so Sales scopes and prices the deal accurately and nothing you built pre-sale becomes a surprise post-signature. Post-sale: architect and ship in customer specific production Architect and deploy customer-specific configurations of AI Agents that automate complex accounting processes — strengthening internal controls, closing books faster, and removing manual work from real customer environments. Perform deep technical troubleshooting: review and edit Python, write SQL for integrations, and diagnose configuration issues across customer systems. Serve as the functional and technical escalation point for issues involving integrations, data flows, and automation logic — including reviewing and optimizing SQL-based validations and API-driven workflows. Maintain SLAs for response and resolution; document issues comprehensively and escalate to Engineering with complete context. Post-sale: own the customer relationship and drive expansion Lead high-impact transformation engagements with enterprise finance teams — diagnosing operational bottlenecks and identifying where AI Agents create the most leverage. Act as the primary technical point of contact across implementations, engaging directly with CFOs, Controllers, and accounting leaders. Guide and train customer teams through workflow redesign, change management, and the value realization required to scale AI Agent adoption across their organization — and carry that same proof-of-value discipline into expansion and renewal conversations. Travel to customer sites as needed (up to 30-40%) for pre-sale technical evaluation, hands-on deployment, training, and adoption workshops. Ongoing: shape the product and the playbook Be the Voice of the Customer: surface technical feedback, integration requirements, and product gaps to Product and Core Engineering — as input to their roadmap, not a substitute for it. Partner with Product Management on learnings from onsite discovery sessions to design workflows centered on AI Agent deployment and business outcomes. Build the playbook: create implementation guides, expansion frameworks, and reusa
Intuit job ID: 24754
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