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Surge AI

Delivers high-quality AI training data

Staff Product Engineer

Software EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • At least 7 years of experience building and shipping production software.
  • Demonstrated experience leading projects with significant scope and ambiguity.
  • A product-focused mindset, including the ability to rapidly ship features and products that solve real-world problems.
  • A commitment to AI ethics and making AI models safer and more useful for society.
  • Ability to clearly explain decisions and trade-offs to other senior engineers.
  • Experience with Python or Ruby on Rails, and React or TypeScript.
  • A Computer Science degree from a top school or an equivalent STEM background.

Responsibilities

  • Develop solutions to enhance annotation efficiency using AI advancements.
  • Create interfaces for data exploration and visualization, including tools for reinforcement learning from human feedback (RLHF) processes.
  • Design and implement systems for large-scale data processing to support AI model training and evaluation.
  • Build interactive products to communicate complex instructions to the workforce.
  • Contribute to products that facilitate the collection and analysis of human feedback for RLHF and other AI training methodologies.

Desired Qualifications

  • Startup experience; early-stage engineering or founder experience is strongly preferred.

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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