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Surge AI

Delivers high-quality AI training data

Legal Operations / Paralegal

Legal Counsel & Legal SupportConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • At least 5 years of experience in legal operations or as a corporate paralegal, at a law firm, in-house, or both.
  • Experience with corporate filings and governmental follow-up, and a reliable system for tracking deadlines.
  • Experience managing vendors, budgets, or billing review.
  • Familiarity with legal technology tools and an interest in using artificial intelligence to build better workflows.
  • Clear written communication and good judgment with confidential information.
  • Comfort building processes in a fast-moving environment and figuring things out as needed.

Responsibilities

  • Manage corporate filings and records, and follow up with government agencies and other organizations to obtain documents and approvals.
  • Track legal deadlines, renewals, and obligations across the company.
  • Run the process for receiving and handling legal requests, making their status, owner, and next steps visible to the team.
  • Improve and document legal processes and workflows.
  • Forecast and oversee the legal budget, including reviewing outside counsel invoices.
  • Manage legal vendors and billing.
  • Evaluate, set up, and operate legal technology, and build new workflows with artificial intelligence tools where useful.
  • Support lawyers on matters as needed.

Desired Qualifications

  • Experience building workflows with artificial intelligence tools is a plus.

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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