Tonal - Staff Machine Learning Engineer, Sensors & Intelligence
Requirements
• A proven independent researcher with a strong track record of publications in relevant venues (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, IMWUT/UbiComp, CHI, IEEE TBME, J. Biomechanics, or similar) • PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Biomechanics, or a related field strongly preferred; we also welcome candidates whose research experience and publication record demonstrate equivalent depth • 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 • Strong grounding in signal processing, including filtering, calibration, time alignment, and frequency-domain analysis • Rigorous experimental design: you know how to build ablations, avoid leakage across subjects, and tell real gains from noise • Ability to independently define and drive ambiguous research problems toward measurable product impact • 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 • Familiarity with musculoskeletal modeling tools (e.g., OpenSim, MuJoCo, MyoSim) or physics-informed ML • Experience with wearable physiological signals such as IMU, PPG, HRV, or EMG data • Experience with personalization, few-shot adaptation, or uncertainty quantification • 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. Should you have any accommodation requests, please reach out to us via our confidential email, [email protected]. All requests will be addressed and responded to in accordance with Tonal’s Accessibility Policy and local legislation.
Responsibilities
• 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 • Develop sensor fusion approaches that handle different sampling rates, sensor noise, synchronization error, and missing modalities • Advance computer vision methods for 2D and 3D human pose estimation, motion reconstruction, and movement quality assessment • Build models that estimate biomechanical and physiological quantities such as joint kinematics, load, effort, fatigue, and recovery • Define evaluation frameworks and benchmarks that measure how well models generalize across users, body types, movements, and equipment conditions • Validate model outputs against gold-standard references such as motion capture, force plates, and clinical-grade physiological sensors • 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
Benefits
• CA$160K – CA$185K • Offers Equity • We take into consideration an individual’s background, experience, and location in determining their salary. This role is also eligible for health insurance, retirement savings benefits, life insurance and disability benefits, flexible paid time off, parental leave, and other additional benefits.
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