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Research Scientist, 3D Computer Vision

  • Toronto, ON, Canada

Full-time

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Job details

Job type
Full-time
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Posting language
English
Posted

Original job description

Research Scientist, Hand Tracking & Manipulation

Location: New York, Toronto or Vancouver (on-site) 

Type: Full-time · Openings: 1 

Salary: US$170K – $210K + equity

The role

Architect and train proprietary foundation models from scratch for 3D hand tracking and articulated pose estimation. This role targets a critical bottleneck in embodied AI: dexterous manipulation and hand-object interaction. You'll build in-house equivalents to leading 3D hand and mesh recovery architectures, plus robust interaction models for heavily occluded egocentric manipulation. You'll train on a large, continuous stream of proprietary ground-truth manipulation data.

What you'll do

  • Design, implement and train state-of-the-art networks for 3D hand pose estimation, dense mesh recovery and kinematic tracking
  • Scale multi-view and temporal architectures across multi-GPU clusters on massive datasets of hands in action
  • Develop novel loss functions that enforce biomechanical constraints, temporal smoothness and physical plausibility
  • Build models robust to motion blur, severe self-occlusion and rapid rotation in first-person manipulation
  • Track objects through complex grasps, segment tools from hands, and map contact points and forces for downstream robotics models
  • Rapidly prototype models for tactile-visual fusion, fine-grained action segmentation and new hardware sensors
  • Feed tracking outputs into pipelines that reason about contact surfaces and object affordances, bridging human video and robot control policies

What you bring

  • Deep expertise in deep learning, 3D computer vision and articulated tracking / hand pose estimation
  • Proven experience training large-scale vision models from scratch, beyond inference or fine-tuning existing checkpoints
  • Strong grasp of parametric hand models (e.g., MANO), inverse kinematics and dense mesh estimation
  • Mastery of PyTorch and deep learning scaling frameworks
  • Experience curating multi-terabyte image and video training datasets
  • Comfort in a fast-paced environment where priorities shift with new research or hardware

Strong signals

  • First-author publications at CVPR, ICCV, ECCV or NeurIPS on 3D hand tracking, hand-object interaction, dexterous manipulation or human mesh recovery
  • Work with egocentric manipulation datasets (e.g., Ego4D, Ego-Exo4D, DexYCB, EPIC-KITCHENS)
  • Custom CUDA kernels for 3D operations, differentiable mesh rendering or collision/contact computation

Please note: Research Scientist roles require highly specific expertise. Apply to only one research role; you may also apply to non-research roles.

More about this job

Responsibilities

Design and train large-scale models for 3D hand pose estimation, mesh recovery, and kinematic tracking, including robust performance under occlusion and rapid motion. Develop hand-object interaction and contact reasoning systems, and connect tracking outputs to robotics models and control policies.

Requirements

Requires deep expertise in deep learning, 3D computer vision, articulated tracking, and hand pose estimation, with proven experience training large-scale vision models from scratch. Candidates should be proficient in PyTorch and scaling frameworks, understand parametric hand models and inverse kinematics, and have experience curating large image and video datasets.

Skills

  • Deep Learning
  • 3D Computer Vision
  • 3D Hand Pose Estimation
  • Articulated Tracking
  • Dense Mesh Recovery
  • Kinematic Tracking
  • PyTorch
  • Multi-GPU Training
  • Parametric Hand Models
  • Inverse Kinematics
  • Dataset Curation
  • Egocentric Vision
  • Hand-Object Interaction
  • CUDA
  • Differentiable Rendering
  • Tactile-Visual Fusion

Benefits

  • Equity

Visa sponsorship

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Categories

  • Science & Research
  • Technology
  • Software
  • Engineering

Keywords

  • 3D Computer Vision
  • Deep Learning
  • Hand Tracking
  • Articulated Pose Estimation
  • Foundation Models
  • Hand-Object Interaction
  • Dexterous Manipulation
  • Dense Mesh Recovery
  • Kinematic Tracking
  • Multi-View Architectures
  • Temporal Architectures
  • Multi-GPU Clusters
  • Biomechanical Constraints
  • Motion Blur
  • Self-Occlusion
  • Egocentric Vision
  • Object Tracking
  • Contact Point Estimation
  • Tactile-Visual Fusion
  • Action Segmentation
  • Robotics
  • PyTorch
  • MANO
  • Inverse Kinematics
  • CUDA Kernels
  • Differentiable Mesh Rendering
  • Ego4D
  • Ego-Exo4D
  • DexYCB
  • EPIC-KITCHENS

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