Head of Revenue Operations
Thinking Machines Lab · San Francisco
Frontier labonsitedirector$300k–$350k
At a glance
Highlights
- Own end-to-end revenue systems from first touch to expansion as first RevOps hire.
- Unify CRM, deal desk, reporting and billing into one coherent operating system.
- Make new AI products and pricing operationally ready from quoting to billing.
What they offer
- Generous health, dental, and vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support as needed
Ideal candidate
Senior revenue operations builder who thrives amid ambiguity and pairs strategy with hands-on execution. You care about creating efficient, trusted go-to-market systems that scale frontier AI products.
Requirements
- 12+ years in revenue operations, sales operations, or GTM systems at high-growth SaaS, API, or infrastructure companies, including leading the function
- Deep hands-on CRM administration 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 GTM processes and systems from scratch
- Hands-on experience leading full system implementations from vendor selection through rollout
- Strong SQL and data skills with ability to build own reporting and analysis
- Ability to operate independently, prioritize across competing demands, and deliver hands-on before building a team
- AI fluency and active use of AI tools in own work
- Exceptional written and verbal communication with ability to partner with GTM 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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