Lead multimodal machine learning research at Tonal, using sensor fusion and computer vision to model human movement and physiology remotely from Toronto. The role is remote-first.

As a Staff Machine Learning Engineer, you will build foundation models from synchronized sensor, video, and physiological signals. You will shape research directions, run rigorous experiments, and translate findings into adaptive coaching, form feedback, personalized progression, and musculoskeletal health models. You will also publish and mentor across the Sensors & Intelligence team.

Key Responsibilities

Lead multimodal foundation model research and development

Design self-supervised pretraining for heterogeneous time series

Develop sensor fusion for noise, alignment, and missing modalities





Advance 2D and 3D human pose estimation methods

Adapt research models for cloud and on-device inference

Requirements

Solid publication record as an independent researcher

Deep expertise in sensor fusion, biomechanics, physiology, or computer vision

Hands-on transformer architecture design, training, and debugging

End-to-end PyTorch, JAX, or TensorFlow experience

Strong signal processing and rigorous experimental design grounding

Shape Tonal’s movement intelligence through foundation models, sensor fusion, computer vision, and rigorous machine learning research.

📌 Staff Machine Learning Research Engineer (Winnipeg)
🏢 Tonal
📍 Winnipeg