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🏢 Baseten · 📍 New York · 💼 hybrid · ⏰ FullTime

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Position Summary & Responsibilities

ABOUT BASETEN

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

THE ROLE

We're looking for an experienced recruiter to help support our scaling sales, marketing and revenue operations teams. You'll work closely with hiring managers and Sales leadership to build out the team, with a focus on Account Executives. You'll own ambitious hiring goals and get deep on our product and the AI infrastructure space, so you can speak credibly to candidates about what we build and why it matters.

RESPONSIBILITIES

Full-cycle recruiting: Own Sales hiring goals and process, from role kickoff through offer acceptance
Sourcing: Work with hiring managers to define what "excellent" looks like for each role, then build pipelines through targeted sourcing and creative outreach
Candidate experience: Partner closely with recruiting coordination on delivering an exceptional candidate experience, from first reach out to offer close
Process improvement: Refine and scale our recruiting process to move faster while keeping a high bar

REQUIREMENTS

3+ years of full-cycle recruiting experience, ideally at a fast-growing startup with big headcount goals
Proven track record of high-volume sales hiring, especially Account Executives

🎯 Candidate Preparation Guide For Baseten Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Evaluates systems-level engineering prowess, deep understanding of ML inference infrastructure, and performance optimization for heavy models.

📋 Preparation Priorities (Key Areas to Clarify):

1. Treating ML infrastructure as standard CRUD web application development
2. Lack of familiarity with container orchestration, networking, and hardware constraints in production
3. Proposing bloated solutions that ignore the economic reality of running heavy LLMs at scale

✨ Recommended Role Alignment Hook:

I obsess over squeezing every ounce of latency and cost out of GPU inference clusters without sacrificing model reliability.

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