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ML Engineer - Reconstruction
Summary by AIEngineer.careers
ML Engineer - Reconstruction is a Computer Vision Engineer role (full-time). with Peripheral Labs. in TORONTO, CA. Compensation shown: $67K–$100K (est.). Imported listing (source: jobs.entrepreneurs.utoronto.ca). Apply on the employer's site (jobs.entrepreneurs.utoronto.ca).
Imported listing (source: jobs.entrepreneurs.utoronto.ca) · Apply on jobs.entrepreneurs.utoronto.ca
TORONTO · CA
$67K–$100K (est.)
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from jobs.entrepreneurs.utoronto.ca
Peripheral Labs is developing spatial intelligence for live sports and entertainment, focusing on 3D computer vision and neural rendering. As an ML Engineer - Reconstruction, you will design and evaluate experiments in 3D reconstruction, neural rendering, and spatial models, and help transition promising research into production. This role is ideal for someone with a strong research background and a desire to see their work applied at scale.
Responsibilities
- Design, run, and analyze experiments in 3D reconstruction, neural rendering, and spatial models.
- Implement and evaluate new techniques from research literature and your own ideas.
- Move promising research into production by profiling, optimizing, and handing off models.
- Collaborate with the team and communicate results clearly.
Requirements
- Master's or PhD in Computer Science, Robotics, Computer Vision, or related field.
- Publication(s) at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, 3DV) in 3D reconstruction, neural rendering, or spatial modeling.
- Strong research experience in 3D reconstruction and spatial modeling (classical and neural approaches).
- Proficiency in Python and a deep learning framework.
- Strong communication skills.
Peripheral Labs
Toronto · CA · 15+ employees
Peripheral Labs is a Toronto-based sports-technology startup founded in 2024 by former self-driving-car experts. It applies autonomous-vehicle perception, sensors, computer vision, and AI-driven 3D/neural rendering to live sports, creating photorealistic volumetric video, immersive replays, and analytics for broadcasters, leagues, teams, and fans.
