Software Engineer Intern (AI/ML), PTLab

CollaboratorTechSoftware26-PTLab-3805

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Job description

A Software Engineer (AI/ML) Intern at PT Lab will work at the intersection of code and physical systems. You will contribute directly to building the MVP of our True Core System (TCS) — the software backbone of our physiotherapy robotics platform — while also helping optimize the data engineering pipelines that power our Embodied AI track. This is a hands-on, high-ownership role for engineers who want to ship real code that runs on real hardware.

You Will:

Contribute to the development and testing of the TCS MVP, including backend logic, data flows, and integration with hardware control interfaces.
Build and optimize data engineering pipelines for ingesting, processing, and curating high-fidelity physical interaction data.
Assist in implementing and validating machine learning components for real-time inference on resource-constrained hardware.
Collaborate with the mechanical, electronics, and integration engineers to ensure software-hardware reliability across the stack.
Write clean, well-documented code and participate in code reviews with the full engineering team.
Support dataset curation and annotation workflows during the 4-month data collection sprint.

Requirement

Basic Qualifications:

Currently pursuing a Bachelor's degree in Software Engineering, Computer Science, AI/Machine Learning, or a related field.
Proficiency in Python and experience with at least one deep learning framework (PyTorch or TensorFlow).
Familiarity with data pipeline tools and practices (ETL, data cleaning, structured storage).
Solid foundation in the mathematics behind machine learning: linear algebra, probability, and optimization.
Ability to work independently, move fast, and communicate clearly in English.

Preferred Skills and Experience:

Prior internship or project experience deploying ML models in non-trivial environments (robotics, embedded systems, or simulation).
Experience with edge AI concepts such as model quantization or deployment on embedded hardware (e.g., Nvidia Jetson).
Familiarity with ROS/ROS2 or other robotics middleware.
Experience with version control (Git) and collaborative software development workflows.
A portfolio or GitHub showing practical AI/software projects, especially those involving physical or sensor data.

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