ML Vision Scientist Co-op — Industrial Robotic Vision

The Institute for Performance and Learning; Vancouver; On-site; CAD 40,000 - 50,000; Full time

Job summary

Apera AI, a Vancouver-based leader in robotics, AI, and machine vision, seeks a Machine Learning Applied Scientist (Co-op) for an 8‑month term (Jan 2027 – Aug 2027) to advance 4D Vision. The role is in-person at our Vancouver office, applying ML and CV to robotic part picking and localization in high-speed manufacturing environments.

You will prototype, evaluate, and improve models deployed on factory floors, collaborating with cross-functional teams to ensure production-ready performance and

Qualifications

• Proficiency in Python and ML frameworks (e.g., PyTorch).
• Understanding of computer vision fundamentals (detection, segmentation, 3D geometry).
• Familiarity with model training, tuning, and evaluation workflows.
• Interest in robotics or applying ML in production-grade software.

Responsibilities

• Prototype and evaluate vision models for object detection, depth estimation, or 6-DoF pose estimation. Benchmark performance on internal datasets reflecting real manufacturing conditions.
• Translate research into production-relevant improvements and prototype methods from recent ML/CV research.
• Enhance synthetic data generation for model training to support generalization across production use cases.
• Collaborate with robotics and software engineers to deploy models on factory floors and debug deployment issues.

Skills

Python

PyTorch

Computer vision

3D geometry

Tools

Linux

Docker

Job description

Apera AI, a Vancouver-based leader in robotics, AI, and machine vision, seeks a Machine Learning Applied Scientist (Co-op) for an 8‑month term (Jan 2027 – Aug 2027) to advance 4D Vision. The role is in-person at our Vancouver office, applying ML and CV to robotic part picking and localization in high-speed manufacturing environments.

You will prototype, evaluate, and improve models deployed on factory floors, collaborating with cross-functional teams to ensure production-ready performance and