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Surge AI

Delivers high-quality AI training data

AI Operations Engineer

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

About the Job

Requirements

  • A background in computer science, engineering, or another quantitative field is preferred.
  • Hands-on coding experience in Python or a similar language, with the ability to build tools and systems from scratch and design solutions to operational problems.
  • A proven track record of navigating ambiguity and executing complex, cross-functional projects in a high-performing environment.
  • Strong interpersonal skills to influence without authority and build cross-organizational alignment.
  • Comfort working through trade-offs, balancing competing priorities, and considering changed perspectives.
  • A deep interest in AI systems; direct AI or machine learning experience is valued but not required.

Responsibilities

  • Design and build scalable systems and tooling, including data processing pipelines, automation workflows, and AI-based quality control tools, collaborating with engineering to solve operational problems.
  • Establish and expand scalable processes to enhance customer support and value delivery.
  • Drive execution of multiple simultaneous cross-functional projects, setting timelines and managing deadlines.
  • Interface with technical and non-technical stakeholders.
  • Own the end-to-end design and execution of data collection projects for frontier AI labs, defining task structures, building quality frameworks, and shipping datasets that improve advanced AI models.

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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