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Staff Machine Learning Engineer

Tonal

Staff Machine Learning Engineer

Tonal

San Francisco, CA

·

On-site

·

Full-time

·

1w ago

Required Skills

Python

Machine Learning

PyTorch

TensorFlow

JAX

Time Series Modeling

MLOps

Who We Are:

Tonal is the world’s first all in one home gym with a simply stunning design. It has completely revolutionized the fitness journey, with adaptive weight and coaching cues powered by advanced AI technology. We’ve united a diverse team of experts and decades of research to reinvent strength training, making it more efficient, more effective, and more engaging.

With this in mind, we want to bring that same innovative approach to the workplace. At Tonal, we continue our shift of emphasis by growing our instrumental team. We collectively weave our knowledge and creativity, as we redefine the future of fitness, and Power Progress for our members.

Overview:

Tonal is looking for a Staff Machine Learning Engineer 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 engineering. You will be responsible for building intelligent systems 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. You will help transform this data into production grade models and systems that deliver real time insights, predictions, and feedback to our members.

What You Will Do:

  • 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

Who You Are:

  • A self driven AI or ML Engineer passionate about bringing applied machine learning into real world, user facing products

  • 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 Py Torch, Tensor Flow, 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

Extra Credit

  • Experience fine tuning large transformer or multimodal models

  • Experience deploying models to real time or edge environments such as on device inference

  • Experience with Go Lang, 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

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, accessibility@tonal.com. All requests will be addressed and responded to in accordance with Tonal’s Accessibility Policy and local legislation.

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About Tonal

Tonal

Tonal

Series E

Tonal is a fitness technology company that manufactures an electromagnetic resistance-based home gym system with an integrated touchscreen display.

201-500

Employees

San Francisco

Headquarters

$1.6B

Valuation

Reviews

3.0

10 reviews

Work Life Balance

3.8

Compensation

2.5

Culture

2.2

Career

3.0

Management

1.8

35%

Recommend to a Friend

Pros

Great product/technology

Good work-life balance and flexible time off

Supportive team and fantastic co-workers

Cons

Poor upper management and leadership

Multiple rounds of layoffs and instability

Micromanaging and role-playing prioritized over results

Salary Ranges

0 data points

Junior/L3

L3

Junior/L3 · UX Researcher

0 reports

$165,000

total / year

Base

-

Stock

-

Bonus

-

$140,250

$189,750

Interview Experience

2 interviews

Difficulty

3.5

/ 5

Duration

14-28 weeks

Experience

Positive 0%

Neutral 50%

Negative 50%

Interview Process

1

First Round Interview

2

Second Round Interview

3

Take Home Assignment