Tonal is looking for a Staff Machine Learning Engineer to join our Sensors & Intelligence team and help build foundation models of human movement and physiology. You'll work where machine learning, biomechanics, physiology, and computer vision meet. You'll have access to the largest strength training dataset in the world: millions of workouts captured through cable force and position sensors, cameras, and wearable trackers.

Your job is to turn that multimodal data into general-purpose representations of how people move, exert effort, fatigue, and adapt over time. Those representations will power the next generation of Tonal's intelligence: adaptive coaching, form feedback, personalized progression, and advanced musculoskeletal health models. This role combines foundational research with work that reaches our members, and you'll help shape that balance as the team grows.

You'll contribute to defining ML research directions, run rigorous experiments, and publish your work, while partnering with engineering to bring breakthroughs into the product. This role is remote-first, based in Toronto, Ontario. Lead research and development of multimodal foundation models that learn from synchronized sensor, video, and physiological signals Design self-supervised pretraining strategies for long, heterogeneous time series data Advance computer vision methods for 2D and 3D human pose estimation, motion reconstruction, and movement quality assessment Work with ML Engineering to adapt research models for cloud deployment and on-device inference Publish regularly at top venues and represent Tonal in the research community through papers, talks,



and collaborations Mentor scientists and engineers and help set the scientific direction of the Sensors & Intelligence team PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Biomechanics, or a related field strongly preferred; Deep expertise in at least one of the following: sensor fusion, biomechanics, physiological signal modeling, or computer vision for human motion Hands-on experience designing, training, and debugging transformer architectures, including attention mechanisms, positional encoding for time series, and multimodal tokenization Proven ability to train and develop ML models end to end in PyTorch, JAX, or TensorFlow, from data curation through experimentation and analysis Robust grounding in signal processing, including filtering, calibration, time alignment, and frequency-domain analysis Clear communicator who can explain complex findings to scientists, engineers, and product leaders Experience pretraining or scaling foundation models, including distributed training and an understanding of scaling behavior OpenSim, MuJoCo, MyoSim) or physics-informed ML Experience with wearable physiological signals such as IMU, PPG, HRV, or EMG data Experience with model compression or efficient architectures for edge deployment Background in human subjects research, data collection protocols, or validation studies Interest in fitness, sports science, or digital health Tonal is committed to meeting the diverse needs of people with disabilities in a timely manner that is consistent with the principles of independence, dignity, integration, and equality of opportunity.

All requests will be addressed and responded to in accordance with Tonal’s Accessibility Policy and local legislation. #

📌 Staff Machine Learning Engineer, Sensors & Intelligence (Winnipeg)
🏢 Tonal
📍 Winnipeg