Head of Revenue Operations
Thinking Machines Lab · San Francisco
Frontier labonsitedirector$300k–$350k
At a glance
Highlights
- Architect the systems and data powering GTM from first touch to expansion.
- Unify CRM, deal desk, reporting and billing into one operating system.
- Design quote-to-cash for usage-based and fixed-price self-serve and enterprise.
What they offer
- Health, dental, and vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support as needed
Ideal candidate
A decisive, hands-on revenue operations leader with 12+ years scaling complex SaaS GTM engines who thrives in ambiguity and cares about building world-class systems that turn frontier AI into efficient growth.
Requirements
- 12+ years of experience in revenue operations, sales operations, or go-to-market systems roles at high-growth SaaS, API, or infrastructure companies, including leading the revenue operations function
- Deep hands-on CRM administration experience, including pipeline management, forecasting, and reporting
- Experience supporting usage-based pricing, including prepaid commitments, credits, and overages, as well as subscription or hybrid models
- Experience designing sales compensation plans and implementing a commissions tool
- Track record of personally building go-to-market processes and systems from scratch
- Hands-on experience leading full system implementations, from vendor selection and requirements through testing and rollout
- Strong SQL and data skills, with the ability to build your own reporting and analysis
- Demonstrated ability to operate independently, prioritize across competing demands, and deliver hands-on before building a team
- AI fluency: actively use AI tools in your own work and excited to apply them to go-to-market operations
- Exceptional written and verbal communication, with the ability to partner credibly with go-to-market leaders and executives
- 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
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