Machine Learning Applied Scientist (Co-op)

Apera is an innovative, Vancouver-based company at the forefront of robotics, AI, and machine vision — recognized with the 2025 Frost & Sullivan Technology Innovation Leadership Award and the 2024 BC Tech "Company of the Year, Growth" award. We're on a mission to redefine AI-driven robotic vision. Apera AI helps manufacturers make their factories more flexible and productive. Robots enhanced with Apera's software have 4D Vision — the ability to see and grasp objects with human-like capability. Challenging applications such as bin picking, sorting, packaging, and assembly are now open to fast, precise, and reliable automation. We work with the world's leading automotive OEMs and Tier 1 suppliers. Our portfolio spans Vue (our 4D Vision software), Forge (a no-code simulation and AI training studio where customers build, validate, and de-risk robotic cells before any hardware is bought), and VuePod, our new turnkey, productized bin-picking cell. Role Overview   Apera AI is seeking a Machine Learning Applied Scientist (Co-op) for the 8 months term period (Jan 2027 - August 2027) to support the development of our 4D Vision Technology used by industrial robots to perform fast, precise tasks in manufacturing environments. This role is based in-person at our Vancouver office. In this role, you will apply machine learning and computer vision techniques to real-world challenges like robotic part picking and localization in structured, high-speed applications. You’ll prototype, evaluate, and improve models that are deployed on factory floors in industries such as automotive and industrial manufacturing. Employee Value Proposition (EVP) Purpose  : You’ll contribute to the intelligence behind robotic systems that perform precise, high-speed automation tasks such as part picking and placement for stamped metal components or machined assemblies. Growth: You’ll gain hands-on experience applying academic concepts to production workflows and working with internal datasets, building robust models, and learning from system behavior in real deployments.   Motivators: You’ll be part of a collaborative, fast-moving team, and see your models tested in simulation and on real industrial robots used in customer-facing solutions. Major Objectives   Prototype and Evaluate Vision Models Within the first 90 days, implement machine learning models for object detection, depth estimation, or 6-DoF pose estimation. Benchmark performance using internal datasets that reflect real manufacturing conditions. [Tools: PyTorch, internal GPU cluster, dataset tools] Translate Research into Production-Relevant Improvements Identify and prototype methods from recent ML or computer vision research. Adapt them to our application domain and evaluate them against production baselines. Document findings and trade-offs. [Focus: Model speed, stability, accuracy under varying lighting and part geometry] Enhance Synthetic Data Generation for Model Training…