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. We are open to candidates based in Canada and the US.
What You Will DoLead 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
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
At Tonal, we believe that the unique and varied lived experiences of our teammates contribute to our overall strength.
At Tonal, we believe that the unique and varied lived experiences of our teammates contribute to our overall strength. We don’t just appreciate differences, we celebrate them, and we always seek people that represent a wide variety of backgrounds. We’re dedicated to adding new perspectives to the team and designing employee experiences that contribute to your growth as much as you do to ours. If your experience aligns with what we’re looking for (even if you don’t check every single box), send us your application. We would love to hear from you!
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.
