Intern AI Systems (Toronto)

Job description

COMPANY OVERVIEW: Birdseye’s mission is to help make the world a safer place! We are the leader in AI-driven Remote Facility Supervision solutions for the Trucking and Logistics Industry. Our Maximum Telepresence Approach™ delivers cutting-edge, data-driven security and operational insights that create a safer working environment for millions of people every year. We provide end-to-end, cutting-edge hardware and AI-driven solutions, redefining security beyond traditional gatehouses. By empowering our professionally trained monitoring agents, we achieve 99.99% reporting accuracy in mission-critical security, safety, and operational support -24/7/365- for some of the world’s largest logistics facilities, as well as small and medium-sized businesses. If you're ambitious, thrive in a fast-paced environment, and align with our ICARE values, Birdseye will be the perfect fit for you. If you’re wondering what the world will look like in 10 years - join our Team! About the Role: We are looking for an Intern (AI Systems) to help design and build agent-based systems that can reason, use tools, evaluate outcomes, and improve through feedback. We are especially interested in ., ., and Ph.D. students in relevant technical fields. Candidates with a strong portfolio of projects are also encouraged to apply. What You'll Work On: 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 What We're Looking For: We are looking for someone who understands modern AI systems and agent design, and is 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 Who Should Apply: -We care more about what you have built and how you think about AI systems than your title or degree. Graduate students are ideal, but undergraduates and self-taught candidates with strong AI projects are also encouraged to apply -To apply: Please include your resume/CV, portfolio or GitHub, and 1-3 projects or research efforts you are particularly proud of. This role will require the candidate to complete background checks (includes but not limited to: references, educational verification, criminal check, credit check - where applicable). Certain roles may also require testing. #LI-Hybrid