Technical Advisor Specialist (part-time Internship)

Scale AI Scale AI · Data AI · San Francisco, CA · University

Internship role for university students to contribute to generative AI projects, focusing on training models for complex reasoning and identifying failure modes. Involves participation in focus groups and team-based projects, with flexible, remote work.

What you'd actually do

  1. Contribute to Cutting-Edge AI Projects: Work on initiatives aimed at advancing AI capabilities—such as training models for complex reasoning tasks or identifying model failure modes. You’ll gain exposure to state-of-the-art technologies and research, enhancing your technical expertise.
  2. Join Focus Groups: Participate in bi-weekly sessions where you’ll exchange insights, discuss evolving AI techniques, and learn from experienced researchers. This forum encourages deep exploration of topics and helps you develop critical thinking and communication skills.
  3. Engage in Team-Based Projects: Collaborate with small groups of specialists on creative challenges—ranging from drafting blog-style content to conceptualizing innovative AI solutions. By working closely with peers, you’ll refine your teamwork, leadership, and problem-solving abilities.
  4. Work Independently & Flexibly: Set your own working hours, ensuring you can prioritize your academic life while still making meaningful progress. The environment is structured to accommodate your schedule without compromising the quality of your contributions.

Skills

Required

  • academic background in computer science, mathematics, engineering, or related STEM field
  • demonstrated coding proficiency
  • strong analytical abilities
  • experience with competitive math or programming competitions

Nice to have

  • curious
  • self-motivated
  • eager to learn
  • contribute
  • collaborate

What the JD emphasized

  • training models for complex reasoning tasks
  • identifying model failure modes

Other signals

  • contribute to cutting-edge AI projects
  • advancing AI capabilities
  • training models for complex reasoning tasks
  • identifying model failure modes