ABOUT THE ROLE:
The AI/CV team is focused on integrating computer vision into fitness
products, enhancing user experience through intelligent coaching and
form assessment. We are seeking a Machine Learning Engineer with a
passion for developing AI-powered consumer products and a proven
track record in computer vision and machine learning.
As a Machine Learning Engineer on Peloton’s Connected Fitness AI
team, you will play a key role in developing form feedback systems,
advancing activity recognition, and enabling intelligent coaching. Your
primary focus will be building temporal models using state-of-the-art
machine learning techniques and tools to power next-generation fitness
experiences.
YOUR DAILY IMPACT AT PELOTON
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● Provide expertise/guidance to solve problems in the computer
vision domain relating to person perception and understanding.
● Work with other team members to build/train/deploy deep
learning models.
● Hypothesize, design, and conduct various experiments on how
we can use novel architectures or new sensors to improve
baseline models.
● Provide help to build tools for model training and diagnostics
● Willing to learn and passionate about fitness.
YOU BRING TO PELOTON
● MS or PhD in Computer Science or a related quantitative field
with a focus on computer vision, machine learning and
mathematics.
● Strong background in software engineering.
● 2+ years of experience developing solutions using supervised,
self-supervised and unsupervised machine learning techniques
in computer vision applications.
● Deep understanding of various neural network architectures
specifically applied to solve CV problems, such as
CNN/LSTM/3DConvs/GNN/TCN/Metric Learning and
transformer based architectures.
● Should have a deep understanding of concepts like object
detection, segmentation, conditional classification, pose
estimation and optical flow.
● Experience with multiple technologies from the following list:
PyTorch, Python, Sagemaker, MLFlow, Airflow, Kubernetes,
AWS
Bonus Points:
● Prior proven experience on Transformers
● Experience with Pose Estimation techniques
● Experience with C++ and edge deployment
● Experience understanding fairness and bias pertaining to model
development
● Experience with infrastructure and MLOps
ABOUT PELOTON:
Peloton (NASDAQ: PTON) provides Members with expert instruction, and world class content to create impactful and entertaining workout experiences for anyone, anywhere and at any stage in their fitness journey. At home, outdoors, traveling, or at the gym, Peloton brings together innovative hardware, distinctive software, and exclusive content. Founded in 2012 and headquartered in New York City, Peloton has millions of Members across the US, UK, Canada, Germany, Australia, and Austria. For more information, visit www.onepeloton.com.
Peloton is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. Equal employment opportunity has been, and will continue to be, a fundamental principle at Peloton, where all team members, applicants, and other covered persons are considered on the basis of their personal capabilities and qualifications without discrimination because of race, color, religion, sex, age, national origin, disability, pregnancy, genetic information, military or veteran status, sexual orientation, gender identity or expression, marital and civil partnership/union status, alienage or citizenship status, creed, genetic predisposition or carrier status, unemployment status, familial status, domestic violence, sexual violence or stalking victim status, caregiver status, or any other protected characteristic as established by applicable law. This policy of equal employment opportunity applies to all practices and procedures relating to recruitment and hiring, compensation, benefits, termination, and all other terms and conditions of employment. If you would like to request any accommodations from application through to interview, please email: applicantaccommodations@onepeloton.com.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance and the San Francisco Fair Chance Ordinance, as applicable to applicants applying for positions in these jurisdictions.
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