Technical Recruiter
Thinking Machines Lab · San Francisco
CHECKED 11H AGO
Frontier labonsitesenior$200k–$275k
At a glance
Highlights
- Own full-cycle hiring for the infrastructure behind frontier models and Tinker.
- Partner with infrastructure leaders to shape interview loops and close top talent.
- Help build recruiting playbooks that scale with a growing infrastructure org.
What they offer
- Generous health, dental, and vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support as needed
Ideal candidate
A high-ownership recruiter energized by infrastructure hiring who goes deep to earn engineers' trust and thrives closing specialized talent in competitive markets.
Requirements
- 4+ years of full-cycle technical recruiting experience, with a track record of hiring for infrastructure, platform, or systems engineering roles
- Ability to hold a substantive technical conversation about infrastructure domains such as distributed systems, cluster or cloud networking, container orchestration, and systems languages
- Experience partnering directly with hiring managers on intake, bar calibration, and interview loop design
- Sound independent judgment on candidate quality, and the ability to manage multiple concurrent searches with minimal oversight
- Experience recruiting at a high-growth AI, machine learning, or infrastructure-focused technology company
- Proficiency with an applicant tracking system (e.g., Greenhouse or Ashby) and modern sourcing tools
- Experience building sourcing strategy or recruiting processes from scratch at an early-stage or fast-scaling team
- A genuine interest in Thinking Machines' mission and in the role a world-class infrastructure team plays in achieving it
- visa sponsorship
- We sponsor visas
Lab profile
About Thinking Machines Lab
Builds frontier AI that extends human will and judgment through customizable models and interfaces.
What they build
- Inkling frontier models
- Tinker model customization
- Human-AI communication interfaces
Known for
- Frontier model training
- Post-training and reinforcement learning
- Evaluation infrastructure
- GPU supercomputing and reliability