Staff Applied Scientist, Tonal

• A self-driven machine learning scientist with experience owning applied AI projects end-to-end and a passion for building meaningful real-world products
• 7+ years of experience, or 5+ with a Master’s degree, or PhD with 3+ years of experience in applied machine learning or data science
• Strong background in modeling time-series, human motion, or physical sensor data (e.g. force, velocity, pose estimation)
• Proficient in Python, and familiar with ML frameworks like PyTorch or TensorFlow
• Experience working with large datasets in modern environments like Databricks or Snowflake
• Strong communicator who thrives in cross-functional settings and can simplify complex models for a product audience
• Demonstrated ability to experiment rapidly, iterate thoughtfully, and deliver production-level code

Desirable

• Tonal is looking for a Staff Applied Scientist (Human Performance AI) to help expand Tonal’s intelligence across movements, training modalities, and member goals
• You’ll be joining a high-impact team at the intersection of machine learning, biomechanics, and product innovation, building algorithms that adapt workouts, enhance coaching, and personalize progression using the largest strength training dataset in the world
• With millions of workouts collected across diverse users, and enriched by data from cameras, cable sensors, and external trackers, Tonal has a one-of-a-kind platform for real-world AI
• This role will be instrumental in developing models that enable new modalities and movements, detect fatigue, predict performance, define new training metrics, and learn how to relate to effort, engagement, and outcomes
• Design and build advanced machine learning models and algorithms that power personalized training, form feedback, and intelligent progression
• Analyze movement patterns to identify fatigue, inefficiencies, or injury risk using real-world sensor data
• Define new metrics for training effectiveness, effort, and performance beyond traditional volume or weight lifted
• Apply self-supervised and transformer-based learning methods to extract insight from unlabeled or minimally labeled data
• Prototype and productionize models that enhance Tonal’s coaching capabilities and member experience
• Collaborate with Sports Science, Product, and Software teams to bring new intelligent features to life
• Use analytics to validate model impact, guide feature design, and support continuous learning in deployed models
• Mentor teammates and help shape Tonal’s long-term data science and AI roadmap