Tavily

Tavily

Sales Development Representative, Tavily

Austin, Texas, United States · Senior

Sponsorship not specified$72k-$90kDetected 8 days ago
Data ScienceLLMsRAGAgentic AIA/B TestingSalesOutbound SalesResearch

About the role

  • We're hiring to expand on the immediate success and impact our founding SDR team has had.
  • Qualify opportunities and book meetings for the Sales team, ensuring they are equiped with the correct information to win the deal.
  • Provide structured feedback on signals, workflows, and outputs to help us improve Tavily based on real-life testing.

Responsibilities

  • Build a deep understanding of Tavily's ICP: AI engineers, data science teams, and product leaders building agentic systems to identify where they need grounded, real-time search in their product

Nice to have

  • 1-2 years of Sales, SDR, Analytics or Computer Science experience in a SaaS or tech environment preferred (open to exceptional entry-level candidates).
  • You are in Austin and excited about an in-person office environment (think 4 days per week).
  • Ability to balance high-volume outreach with thoughtful experimentation and feedback.

Compensation

  • We offer competitive compensation and benefits packages.
  • Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.
  • Base Compensation Range
  • $71,700 - $89,600 USD
  • Pay Transparency

Benefits

  • 401(k) plan: Up to 4% company match with immediate vesting.
  • Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
  • Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
  • Company-paid short-term, long-term and life insurance coverage.

Company info

  • Disability & life insurance: Company-paid short-term, long-term and life insurance coverage.

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