Plaud.ai
Global Talent Acquisition - San Francisco (Intern / Contract)
San Francisco, CA · Intern · Internship
Sponsorship not specifiedDetected 195 days ago
NLPComplianceBrand StrategyLogisticsRecruitingPro ToolsHIPAACommunicationCollaborationAdaptability
About the role
- Plaud Inc. is a Delaware-incorporated, San Francisco-based company pushing the boundary of human-AI intelligence through a hardware-software combination.
- With full ISO 27001, ISO 27701, SOC 2, GDPR, EN18031, and HIPAA compliances, Plaud is committed to the highest standards of data security and privacy protection.
Responsibilities
- Plaud is looking for a Talent Acquisition Intern to support junior-role hiring, candidate pipeline building, and recruitment operations.
- They will learn end-to-end recruiting in a fast-paced tech environment while working closely with the TA team to deliver high-quality execution.
- Build and maintain candidate pipelines for junior-level roles through proactive sourcing.
- Manage interview scheduling, candidate communication, and ATS updates with high accuracy.
- Support sourcing experiments and market mapping to broaden talent outreach.
Skills
- Plaud is a bootstrapped, skyrocketing, profitable company with a $250M revenue run rate achieved in just three years.
- Define the next-gen paradigm for human-AI interaction.
- Work with passionate teammates who value innovation, collaboration, and customer success.
- Grow your career in a culture that champions continuous learning and fast career development.
- Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere.
Compensation
- Team & Culture Annual company offsites, team events, and a culture that values craftsmanship, ownership, and velocity.
- $30/h-$40/h base salary+performance bonus.
Benefits
- Competitive Compensation: $30/h-$40/h base salary+performance bonus.
- Strong learning agility and adaptability - able to pick up tools and processes quickly.
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This listing is sourced directly from Plaud.ai's careers page and normalized into a canonical job model.