Hiddenlayer

Hiddenlayer

Customer Success Manager- Federal

Remote, US

Sponsorship not specifiedDetected 4 days ago
TypeScriptCloud PlatformsMachine LearningData ScienceCybersecurityCRMAccount ManagementIntellectual PropertyCustomer SuccessLeadershipCommunicationProblem SolvingCISSP

About the role

  • You will work closely with customers, Product, Engineering, and Go-to-Market teams to ensure successful adoption and operationalization of HiddenLayer solutions.
  • Serve as the primary point of contact for a portfolio of federal customers throughout the customer lifecycle.
  • Coordinate customer engagements to ensure commitments, deliverables, and key objectives are completed on schedule, while maintaining accurate engagement records and status reporting.

Responsibilities

  • Own Customer Relationships & Success
  • Drive Adoption & Mission Outcomes
  • Develop a deep understanding of customer missions, priorities, and operational environments.
  • We are committed to building a diverse team with individuals from various backgrounds, experiences, abilities, and perspectives, and we are proud to be an

Requirements

  • 5+ years of experience in Customer Success, Technical Account Management, or a related customer-facing role.
  • Experience managing complex customer engagements involving multiple stakeholders and mission-critical environments.
  • Experience scaling Customer Success at a Series A/B startup.
  • Experience working with federal acquisition processes, contract vehicles, and government program offices.
  • Experience supporting cybersecurity or AI/ML security solutions.

Nice to have

  • Clearance- Active Top Secret or TS eligible strongly preferred
  • HiddenLayer protects the world's most valuable technologies from adversarial AI attacks.
  • Our dedication to innovation has been recognized by prestigious awards such as RSA's Innovation Sandbox Winner, CB Insights AI 100, CyberTech 100, and SC's Most Promising Early-Stage Start-up.

Skills

  • Technical background in cybersecurity, data science, machine learning, cloud infrastructure, or enterprise security.
  • Strong communication, presentation, and problem-solving skills.

Compensation

  • We offer a generous stipend for your home office setup, annual upgrades to ensure you have a comfortable workspace and a monthly stipend for internet/phone expenses.

Benefits

  • We offer a generous stipend for your home office setup, annual upgrades to ensure you have a comfortable workspace and a monthly stipend for internet/phone expenses.
  • Conduct regular business reviews, success planning sessions, and customer health assessments.
  • Monitor customer health and proactively mitigate risks that could impact customer success, satisfaction, or renewal.
  • Maintain accurate account health, engagement, and success plans within CRM and customer success systems.

Company info

  • We were founded by AI professionals and security specialists with first-hand experience of how insidious adversarial AI attacks can be to detect and defend against.
  • Determined to prove that these attacks were preventable, the team developed a unique, patent-pending, productized solution to support organizations in accelerating their adoption of AI securely.
  • Enjoy unlimited and flexible time off for all salaried employees, in addition to 15 paid company holidays.
  • Experience supporting U.S. Federal Government customers, including DoD, IC, DHS, or civilian agencies.
  • Partner with customers to ensure successful onboarding, deployment, adoption, and ongoing use of HiddenLayer solutions.
  • Advocate for Customers
  • Though we're distributed, we are intentional about getting the team together a couple of times a year.
  • We are committed to providing a workplace free of any discrimination or harassment.
  • We are a completely remote global team.

Equal opportunity

  • equal opportunity employer.
  • Equal Opportunity/Affirmative Action employer.

Visa & Work Authorization

  • Government security clearance

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