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Open nowPosted 568 days ago

Machine Learning Engineer Intern

Putnam Science Academy

Where
Putnam, Connecticut
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Your applicationOpen nowMachine Learning Engineer InternPutnam Science Academy · Putnam, Connecticut

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This job: posted 568 days ago

The posting

AI + Elite Basketball | Summer 2025 Duration: ~8 weeks (June 16 ~ Aug 11) Onsite (Putnam, CT), with housing & meals supported through PSA’s campus Type: Unpaid internship, with potential path into a deeper role beyond summer Open to candidates with CPT, OPT, and H-1B

About Us

This internship is hosted by PAI (Precision Athletics Intelligence), a new AI athletic program by Putnam Science Academy (PSA) — one of the most elite basketball-focused high schools in the U.S.

  • PSA has won 5 National Prep School Basketball Championships, in 8 years, most recently in March 2025
  • The school’s mission is to deliver world-class private high school education while developing players for NCAA and NBA levels
  • PAI is built to bring AI into high-performance sports and education, starting with this summer MVP project

This is PAI’s first technical initiative, aiming to create a foundational performance analysis platform for PSA’s nationally ranked basketball program — with high visibility and real-world application from day one.

What You’ll Build

You’ll join a small, focused team building an end-to-end system to:

  • Automatically analyze practice footage
  • Detect key actions (shooting, movement, defensive effort)
  • Deliver structured feedback to coaches and players within minutes

This product is aiming China Market and it will serve elite athletes and coaching staff immediately, with long-term potential to scale across teams and domains.

Your Role

As an ML intern, you'll work on the core computer vision pipelines that power the system.

Responsibilities

  • Use or fine-tune models like YOLOv8, OpenPose, or MediaPipe
  • Build pipelines to extract training insights from video
  • Process raw frames into structured data (e.g. player tracking, shot detection)
  • Evaluate models on accuracy, reliability, and latency
  • Deliver usable outputs via APIs to frontend/dev teams
  • Write modular, reproducible code for experimentation and iteration

What We’re Looking For

Core Skills

  • Strong Python skills; comfortable with Jupyter, scripting, and code structure
  • Experience with PyTorch or TensorFlow — or fast learning capability
  • Comfortable using OpenCV and working with image/video data
  • Familiar with Git and collaborative development environments
  • Fluent spoken Chinese (Mandarin)

Mindset

We’re looking for someone who:

  • Has real confidence in their ability to learn fast and figure things out independently
  • Can take vague or high-level product goals, and turn them into working code
  • Works through ambiguity with speed, structure, and clarity
  • Cares about doing real work that gets used — not just academic experiments
  • Is genuinely interested in basketball and understands the game at a basic level
  • Thrives in a builder-style environment with ownership, speed, and open problems

Bonus (Not Required)

  • Projects involving video analysis, pose estimation, or CV pipelines
  • Experience with DeepSORT, sports heatmaps, or action recognition
  • Familiarity with serving models via FastAPI, Flask, or REST endpoints
  • Background as a player, coach, or data analyst in sports

Who Can Apply

We welcome candidates from a variety of backgrounds:

  • Undergraduates (junior/senior preferred) with strong project experience
  • Master’s students in CS, AI, or related fields
  • PhD students focused on applied machine learning
  • Self-taught engineers — if you’ve built real things, we want to see them

We value your ability to build and think clearly over your academic label.

Why This Matters

This is not a typical early-stage internship.

While our tech team is just starting out, our platform isn’t. You’ll be building within a system that already has:

  • A championship-level basketball program
  • Immediate real-world users: athletes and coaches with daily training needs
  • A founder with full access to decision-making, facilities, and execution
  • A high-trust environment where things move fast, and feedback is real

In many ways, PSA provides what most startups seek after Y Combinator:

A live environment, institutional support, immediate demand, and the room to build and scale.

If you have:

  • Strong learning ability
  • Clear technical thinking
  • Ambition to turn huge ideas into real systems

…and you're excited by sports, education, AI, and building things from scratch — you’ll thrive here.

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