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Surge AI

Delivers high-quality AI training data

Revenue Operations Manager

Sales OperationsConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • Experience in revenue operations, sales operations, or marketing operations, preferably at a B2B technology or AI company, including hands-on customer relationship management (CRM) selection and implementation.
  • Ability to define funnel stages, design lead routing, and turn messy data into useful reporting.
  • Ability to work across Marketing, Business Development, Operations, and Finance, influence without authority, and follow through on work.
  • Meticulous attention to detail and comfort in a fast-paced, high-ambiguity startup environment.
  • Deep interest in AI systems.

Responsibilities

  • Define funnel stages, lead routing, and follow-up processes so every opportunity is handled consistently.
  • Lead customer relationship management (CRM) strategy, implementation, and administration, and shape the broader revenue technology stack.
  • Partner with Marketing on target account strategy to support account-based marketing campaigns.
  • Build attribution and pipeline reporting that connects marketing activity to opportunities and closed revenue.
  • Support pipeline reviews, forecasting, and recurring business reporting for leadership.
  • Ensure commercial data flows reliably from deal to contract to revenue.

Desired Qualifications

  • Direct AI or machine learning experience is valued but not required.

About the Company

Surge AI

Delivers high-quality AI training data

Surge AI provides high-quality training and evaluation data for AI models. It creates human-annotated datasets, preference data, and reinforcement learning from human feedback (RLHF) to help developers of large language models and other machine learning systems improve accuracy, alignment, and real-world performance, especially for advanced generative AI tasks. The company targets the high end of the data labeling market by prioritizing quality, scalability, and the ability to handle complex, model-critical labeling rather than simple commodity annotations. Its goal is to support better-performing, safer, and more reliable AI systems by delivering data that directly feeds model training and evaluation.

Company size
201-500
Company stage
Not listed
Total funding
Not listed
Headquarters
San Francisco, CA
Founded
2020

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