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Surge AI

Delivers high-quality AI training data

Software Engineer, Security

Security Engineering & ArchitectureConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listed

About the Job

Requirements

  • At least 7 years of experience building and shipping production software, or at least 5 years with exceptional experience.
  • Strong software engineering skills sufficient to meet the company's coding and system design bar for engineers.
  • Competence across product, infrastructure, and corporate security.
  • Ability to operate as a generalist across a broad range of security work.
  • Early-stage startup experience, ideally as one of the first security hires at a company.
  • Ability to work amid ambiguity without a fixed roadmap or product managers and to fix problems directly.
  • A Computer Science degree from a top school or an equivalent STEM background.

Responsibilities

  • Fix authentication and authorization issues across the platform.
  • Improve management of keys and secrets across projects.
  • Harden internal tooling and safely integrate off-band tools built by other teams into the platform.
  • Refactor permissioning and move sensitive capabilities out of admin systems.
  • Contribute to core backend engineering work alongside the engineering team.
  • Own security reviews, incident response, and tooling, working across product, infrastructure, and corporate security as business needs require.

Desired Qualifications

  • Experience with Python or Ruby on Rails.

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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