Head of Revenue Operations

Thinking Machines Lab•San Francisco, CA

1d•$300,000 - $350,000

About The Position

Thinking Machines Lab is hiring its first Head of Revenue Operations to architect and own the systems, processes, and data that power its go-to-market organization, covering the customer journey from first touch through pipeline, contract, and expansion. This role will define how the sales and self-serve businesses operate and provide go-to-market leadership with the infrastructure and insight to scale revenue efficiently. The Head of Revenue Operations will own the function end to end and build the team over time. Initially, the role combines strategic ownership with hands-on execution. The commercial model includes usage-based and fixed-price products sold through self-serve and enterprise channels, with enterprise agreements that may involve prepaid commitments, custom pricing, and mid-term amendments. The role will unify the revenue stack into a coherent operating system spanning CRM, deal desk, reporting, and billing integration.

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, rather than only managing teams that built them.
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 reporting and analysis independently.
Ability to operate independently, prioritize competing demands, and deliver hands-on before building a team.
AI fluency, including active use of AI tools in one's work and enthusiasm for applying them to go-to-market operations.
Exceptional written and verbal communication, with the ability to partner credibly with go-to-market leaders and executives.

Nice To Haves

Experience with HubSpot, Ironclad, Metronome, Stripe, Anrok, ERPs, and Hex.
Experience supporting both product-led and sales-led motions, including usage-based expansion.
Experience at a frontier AI lab, developer platform, or enterprise SaaS organization with complex, multi-product revenue models.
Familiarity with how deal structure affects revenue recognition.

Responsibilities

Own the go-to-market systems strategy and roadmap, including selecting, implementing, and integrating tools.
Own the CRM as the system of record for pipeline, accounts, and deals, including data definitions, field governance, and lead and account routing.
Build and maintain deal desk infrastructure, including quoting, approval rules, and routing for non-standard terms.
Drive forecasting, go-to-market planning, and operating cadence with the Head of GTM and Finance, including segmentation by consumption potential, territories, capacity, and quotas.
Drive sales compensation plan design with the Head of GTM, Compensation, and Finance, and lead implementation of the commissions tool.
Build go-to-market analytics covering funnel conversion, win rates, expansion, and churn, and identify fast-growing self-serve customers for sales.
Drive adoption of new tools and processes across the go-to-market team and run an intake and prioritization process for systems requests.
Partner with product and go-to-market teams to make new products and pricing changes operationally ready, from quoting through billing.
Design the quote-to-cash process and customer data model across GTM, Legal, and Finance so data is entered once and trusted everywhere.
Own billing configuration and integrations that turn signed deals into accurate invoices.
Automate manual work across the revenue stack, including with AI-assisted tooling.
Build and lead the revenue operations team as the business scales.