Senior machine learning engineer building recommendation and personalization systems for Pinterest’s creative discovery platform. Improving ML models across Homefeed, Ads, Growth, Shopping, and Search.
About the role
- Staff ML Engineer developing multimodal foundation models from Tonal’s sensor, video, and physiological workout data. Advancing adaptive coaching, form feedback, and musculoskeletal health intelligence.
Responsibilities
- Lead research and development of multimodal foundation models using synchronized sensor, video, and physiological signals
- Design self-supervised pretraining strategies for long, heterogeneous time-series data
- Develop sensor-fusion approaches addressing sampling rates, sensor noise, synchronization error, and missing modalities
- Advance 2D and 3D human pose estimation, motion reconstruction, and movement quality assessment
- Build models estimating joint kinematics, load, effort, fatigue, and recovery
- Define evaluation frameworks and benchmarks for generalization across users, body types, movements, and equipment conditions
- Validate model outputs against 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 at top venues and represent Tonal through papers, talks, and collaborations
- Mentor scientists and engineers and help set the scientific direction of the Sensors & Intelligence team
Requirements
- Proven independent researcher with a strong track record of publications in relevant venues
- PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Biomechanics, or a related field strongly preferred; equivalent research experience and publication record welcomed
- Deep expertise in 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
- Strong grounding in signal processing, including filtering, calibration, time alignment, and frequency-domain analysis
- Rigorous experimental design, including ablations and avoiding leakage across subjects
- Ability to independently define and drive ambiguous research problems toward measurable product impact
- Clear communication of complex findings to scientists, engineers, and product leaders
- Legally authorized to work in the US and/or Canada
Benefits
- Equity
- Health insurance
- Retirement savings benefits
- Life insurance
- Disability benefits
- Flexible paid time off
- Parental leave
- Other additional benefits
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