Staff Machine Learning Engineer

Tonal is the world’s first all in one home gym, revolutionizing the fitness journey with advanced AI technology. They are seeking a Staff Machine Learning Engineer to expand Tonal’s intelligence across movements and member goals, focusing on building intelligent systems that adapt workouts and enhance coaching using extensive datasets.

Responsibilities:

• Design, implement, and optimize machine learning training pipelines and model serving infrastructure for real time applications
• Develop algorithms and ML models that enable personalized training, adaptive coaching, and performance prediction
• Fine tune and evaluate transformer based or self supervised learning models using Tonal’s multimodal dataset
• Build data driven systems that measure training effectiveness, effort, and progression beyond traditional weight based metrics
• Prototype, train, and deploy ML models that run efficiently at scale or on device
• Collaborate cross functionally with Exercise Science, Product, and Software teams to deliver intelligent features that improve the member experience
• Contribute to the development of automated tools for experimentation, model validation, and continuous retraining
• Write high quality, maintainable Python code and work closely with backend engineers to integrate models into Tonal’s production systems
• Mentor teammates and help shape Tonal’s growing AI and ML best practices

Requirements:

• 7 plus years of experience in software engineering or applied ML, or 5 plus with a Master's degree, or PhD with 3 plus years of experience
• Strong coding skills in Python and experience with frameworks such as PyTorch, TensorFlow, or JAX
• Experienced in ML training, evaluation, and deployment workflows such as Sagemaker, MLFlow, Databricks, or similar
• Deep understanding of time series modeling, human motion, or sensor based learning from devices such as force transducers, position encoders, IMUs, or cameras
• Familiar with MLOps best practices and scalable model training pipelines
• Strong communicator who can collaborate with scientists, product managers, and engineers
• Track record of delivering performant ML systems from prototype to production
• Experience fine tuning large transformer or multimodal models
• Experience deploying models to real time or edge environments such as on device inference
• Experience with GoLang, Kotlin or Flutter
• Experience with distributed training, mixed precision optimization, or model compression
• Interest in fitness, digital health, or intelligent training systems
• Background in biomechanics, kinesiology, or human performance analytics