Machine Learning Engineer - Reconstruction
Peripheral Labs
Who we are:
Peripheral is developing spatial intelligence, starting in live sports and entertainment. Our models generate interactive, photorealistic 3D reconstructions of sporting events, building the future of live media. We’re solving key research challenges in 3D computer vision, creating the foundations for the next generation of robotic perception and embodied intelligence.
We’re backed by Tier-1 investors and working with some of the biggest names in sports. Our team includes top robotics and machine learning researchers from the University of Toronto, advised by Dr. Steven Waslander and Dr. Igor Gilitshenski.
Our team is ambitious and looking to win. We’re seeking a machine learning engineer to push the latency and quality of our 3D reconstruction system.
What you’ll be doing:
Development of our 3D reconstruction method to improve novel view reconstruction quality,
Improving our models for prior generation, such as depth and surface estimation, keypoint matching, and segmentation.
What we’d want to see:
Strong understanding of 3D computer vision,
Past research experience in neural rendering (Gaussian Splatting, NERFs),
Previous industry experience training and deploying ML models,
Ways to stand out from the crowd:
Previous research experience with feedforward, temporal, multi-image models.
Previous experience fine-tuning foundational models such as DINO, Map Anything, etc,
Top publications at conferences like NeurIPS, ICLR, ICML, CVPR, WACV, CoRL, ICRA,
Experience leading high-performance teams,
Why join us:
Competitive equity as an early team member.
$80-150K CAD + bonuses, flexible based on experience.
Exclusive access to the world’s biggest sporting events and venues,
Work on impactful projects, developing the future of 3D media and spatial intelligence.