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Machine Learning Intern

SwingVision, Inc.
Berkeley, USA
Internship, Contract
Entry
Onsite
Discovered 3 weeks ago
PythonPyTorchTensorFlowSwiftCore ML

Full Job Posting

Recent achievements include

• Apple Design Award, Editors' Choice on the App Store, and 4x App of the Day

• Featured in Apple Keynote events , Forbes , Financial Times & The New York Times

• Trusted by 500k+ players, coaches, and federations all across the globe

• $10m+ raised from Sony, Tennis Australia and Andy Roddick

💼 The Role

We are currently seeking a Machine Learning Intern to join our team in Berkeley, CA. You will learn about and contribute to the entire machine learning pipeline from research and training to testing and deployment, driving the computer vision capabilities that power our category-defining app.

Upon successful completion of the internship, a full-time Machine Learning Engineer offer will be extended.

🧰 Responsibilities

  • Assist in designing and developing machine learning models and algorithms.
  • Contribute to data collection, data cleaning, and preprocessing tasks.
  • Support senior engineers in the deployment and monitoring of machine learning models.
  • Participate in team meetings and contribute innovative ideas.

🎯 Requirements

  • Bachelor’s degree in Computer Science, Data Science, or related field.
  • Basic understanding of machine learning principles and techniques.
  • Familiarity with Python and machine learning libraries such as Pytorch and TensorFlow.
  • Strong problem-solving skills and a keen attention to detail.
  • Avid user of SwingVision.

⭐️ Preferred

Experience interning in a Series A or similar high-growth environment (team size <50).

Prior experience in Swift and Apple's Core ML framework.

MS or PhD in Computer Science, Statistics or a related major.

Background in sports analytics, kinematics, or a deep understanding of racket sports mechanics.

SwingVision is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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