Auditoria Ai

Auditoria Ai

Senior Product Manager, Accounts Payable

Santa Clara, USA · Senior

No sponsorshipDetected 53 days ago
OKRsOutbound SalesCommunicationAccounts Payable

About the role

  • We are looking for an experienced enterprise software Product Manager to join Auditoria's Product Management team.
  • It is a hands-on role, based in Santa Clara, California.
  • If hired, employees will be expected to work from the Santa Clara office a minimum of three (3) days per week.

Responsibilities

  • Evaluate market offerings for build/buy fit, and for competitive aspects.
  • Positioning, messaging, sales and field enablement, partner enablement.
  • Participate in any pre-sales and post-sales customer and partner activities representing the products.
  • Collaborate across the Engineering, Pre-sales, Professional Services, Support teams and build a culture of customer-first with unmatched competence, excellence across all the functions.

Requirements

  • Bachelor's degree in information systems or accounting.
  • 5+ years of experience as a Senior Product Manager, with a proven track record of successfully shipping Enterprise SaaS offerings.
  • Experience with AI technology: language models (large and small, proprietary and open source), SDLC of apps that are built using AI tech, UX paradigms for users interfacing with AI.
  • Must be currently authorized to work in the United States without employer sponsorship, as we are unable to sponsor or transfer visas for this position.
  • Must be located in or within commuting distance of Santa Clara, CA to be considered.

Benefits

  • Adept at building consensus across diverse, globally distributed teams and customer communities, utilizing strong interpersonal and influencing skills to deliver strategic vision.

Visa & Work Authorization

  • Must be currently authorized to work in the United States without employer sponsorship, as we are unable to sponsor or transfer visas for this position

This listing is sourced directly from Auditoria Ai's careers page and normalized into a canonical job model.