traversal

AI Adoption Engineer - West

Remote (Remote)fulltimemidAdded today

About this role

Traversal seeks an AI Adoption Engineer to guide enterprise customers through four adoption phases—Activation, Scaling, Optimization, and Value Realization—for its AI SRE platform. You'll own customer outcomes end-to-end, combining technical depth in incident debugging and prompt engineering with executive-level relationship management to drive usage, accuracy, and expansion.

What you'll do

Own adoption roadmap and all customer touchpoints across four phases, from post-deployment through renewal
Run diverse meetings: kickoffs, enablement sessions, technical syncs, QBRs, and executive updates
Engineer custom integrations, debug accuracy failures, and instrument performance tracking with customers
Synthesize customer feedback into structured product roadmap input for engineering teams
Drive technical adoption by embedding Traversal into customer compliance checklists and workflows
Maintain 30/60/90 roadmaps and publish monthly executive updates backed by usage data

What they're looking for

Solutions engineering or technical account management at enterprise software/infrastructure companies
Distributed systems and production incident analysis fundamentals
AI/LLM prompt engineering and debugging
Log analysis and root cause investigation
Executive and engineer-level communication and relationship building
Integration and workflow automation design
Customer adoption and change management
Quarterly business review and strategic planning facilitation

Benefits

Remote work (West-based)
Opportunity to work with world-class team from MIT, Harvard, Berkeley, and top tech companies
Impact on mission-critical production systems for largest enterprises globally
Hands-on technical role with clear customer ownership and measurable outcomes

Likely interview questions

Walk us through a time you drove adoption of a complex technical product—how did you measure success and what obstacles did you overcome?
Describe your experience debugging production incidents in distributed systems. How would you help a customer understand why an AI agent produced an inaccurate root cause?

How do you balance the needs of technical engineers and executive stakeholders in the same customer relationship, and when have you seen tension between them?
Tell us about a time you translated customer feedback into product roadmap changes—how did you validate the problem and communicate the business case?
What's your experience with custom integrations or prompt engineering, and how hands-on do you get versus escalating to specialists?
How would you approach the first 90 days with a new enterprise customer to establish credibility and set up the adoption roadmap?
Describe a situation where a customer wasn't achieving expected value from a platform—what did you do to diagnose and resolve it?

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