Baseten

Baseten

Revenue Strategy & Operations

San Francisco · Mid

Sponsorship not specifiedDetected 45 days ago
SQLMachine LearningSparkA/B TestingCRMCadenceResearchLeadership

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Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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About the role

  • This is a foundational hire on our GTM Strategy & Revenue Operations team, sitting at the intersection of strategic planning and field execution.
  • This is a rare opportunity to be an early GTM strategy and operations hire at one of the fastest-growing companies in AI infrastructure - and to help define how we scale.

Responsibilities

  • Support quota framework development, helping translate top-down targets into field-level plans that are credible and motivating
  • Develop and maintain Baseten's segmentation logic: how we carve the market by industry, company profile, consumption potential, and AI maturity
  • Partner with Central Ops on ICP and TAM database strategy to ensure segmentation is grounded in clean, enriched data
  • Support the PG cadence: track actuals vs. model, surface gaps early, and partner with field leadership to course-correct
  • Play an active role in the weekly forecast process, helping build the business narrative that connects leading indicators to revenue outcomes
  • Build and maintain the analytics layer that powers GTM decision-making, from pipeline dashboards to cohort analyses to efficiency metrics

Requirements

  • 3-5 years in GTM strategy, revenue operations, sales strategy, or a related function, ideally at a high-growth B2B SaaS or infrastructure company

Nice to have

  • Experience with consumption-based or usage-based business models strongly preferred
  • you understand the difference between ARR and revenue, and how to think about drawdown, expansion, and net retention
  • Exposure to a company scaling through a Series B-D inflection point a plus
  • you have a sense of what good looks like and what breaks as you grow
  • Background in consulting and/or banking / finance a plus

Skills

  • Collaborative operating you can define the problem before solving it
  • Bias toward action - you'd rather ship something good and iterate than wait for perfect
  • Curious about the AI infrastructure market; you want to understand what we sell, who buys it, and why
  • Be a foundational member of the GTM team at one of the most important companies in the AI infrastructure space
  • Work on problems that are genuinely hard and genuinely new - consumption-based GTM at AI scale doesn't have an existing playbook
  • High ownership, high visibility, direct access to leadership
  • Competitive compensation, equity, and benefits
  • Strong business judgment: you don't just surface data, you tell people what it means and what to do about it

Compensation

  • Contribute to the annual and quarterly GTM planning process in partnership with Finance, including headcount modeling, ramp assumptions, and revenue target-setting

Benefits

  • Competitive compensation, including meaningful equity
  • (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
  • Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
  • Paid parental leave
  • Fertility and family-building stipend through Carrot
  • Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
  • If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.
  • Build and maintain coverage models aligned to 2-year growth projections, incorporating territory design, account segmentation, and capacity planning
  • Contribute to the design of the pipeline generation model: coverage ratios, qualified pipeline definitions, and input assumptions

Company info

  • At Baseten, we are committed to fostering a diverse and inclusive workplace.

This listing is sourced directly from Baseten's careers page and normalized into a canonical job model.