Intern AI Systems (Dallas)

Birdseye Solutions•Dallas, TX
•Hybrid

About The Position

Birdseye is seeking an Intern (AI Systems) to assist in the design and development of agent-based systems. These systems should be capable of reasoning, utilizing tools, assessing outcomes, and enhancing performance through feedback mechanisms. The company is particularly interested in candidates pursuing M.A.Sc., M.Sc., and Ph.D. degrees in relevant technical disciplines. Individuals with a robust portfolio of projects are also encouraged to apply. Birdseye is a leader in AI-driven Remote Facility Supervision solutions for the Trucking and Logistics Industry, aiming to create safer working environments through advanced security and operational insights.

Requirements

  • Understands modern AI systems and agent design
  • Comfortable thinking about how multiple components work together
  • Designing agent architectures and workflows
  • Working with multi-agent systems
  • Designing feedback and evaluation loops
  • Thinking about tool selection, planning, and task decomposition
  • Building systems that can operate over multiple steps
  • Evaluating where and why agents fail
  • Reading current AI research and applying relevant ideas
  • Building and testing ideas independently
  • Strong AI projects (for undergraduates and self-taught candidates)
  • Resume/CV, portfolio or GitHub, and 1-3 projects or research efforts

Nice To Haves

  • M.A.Sc., M.Sc., and Ph.D. students in relevant technical fields

Responsibilities

  • Designing single-agent and multi-agent systems
  • Building tool-use and function-calling workflows
  • Designing feedback loops for agents to evaluate and improve their outputs
  • Agent planning, memory, delegation, and coordination
  • Building evaluation frameworks for agent performance
  • Designing systems where agents interact with software, data, and other AI models
  • Developing autonomous workflows with human review where appropriate
  • Improving reliability, observability, and failure recovery in agent systems
  • Exploring approaches to long-running and self-improving AI systems
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