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Surge AI

Delivers high-quality AI training data

Total Rewards Lead

Compensation & BenefitsUpdated on Nov 12
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • At least 5 years of experience in Total Rewards, with ownership across benefits, compensation, and equity.
  • A track record of building programs in high-growth companies, including building a benefits program from vendor selection through launch.
  • Experience running merit and promotion cycles and designing the processes behind them.
  • Working knowledge of equity administration at a private, high-growth company.
  • Comfort working with a mixed workforce of hourly and salaried employees in the United States and internationally.
  • Strong analytical skills and the ability to present and defend recommendations to senior leaders.
  • Use of AI and automation in one's work, and comfort building effective initial solutions and refining them later.

Responsibilities

  • Own the benefits program, including vendors, open enrollment, renewals, and leave, for hourly, salaried, and international employees.
  • Document and refine the compensation philosophy, translating it into bands and guidelines for managers and recruiters.
  • Run merit and promotion cycles using a documented process so that only true exceptions reach leadership.
  • Partner with Recruiting on competitive offers and current pay ranges, including a consistent path for contributors moving into employee roles.
  • Run pay equity reviews and serve as the authority on total rewards compliance, including FLSA, ACA, and state mandates.
  • Own day-to-day equity administration and build grant, refresh, and promotion guidelines with Finance and Legal.
  • Build total rewards workflows and market data reporting in HRIS and compensation tools, using automation and AI to stay lean.

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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