JOB TITLE: Machine Learning & Artificial Intelligence Engineering Internship – Summer 2027
As a Machine Learning & Artificial Intelligence Engineering Intern, you will join a multi-disciplinary team of researchers and software developers to build best-in-class solutions for next-generation mobile and embedded machine learning platforms. You will design, develop, and test software for tools and frameworks that make models smaller and more efficient for edge devices, including smartphones, autonomous vehicles, and IoT devices. Your work involves collaborating on AI systems infrastructure, distributed inference platforms, compiler technologies, and GPU acceleration. You will utilize programming languages such as C, C++, and Python alongside frameworks like TensorFlow, TFLite, and PyTorch. Key technical areas include deep neural networks, model compression, quantization, and performance optimization of machine learning workloads, specifically addressing the challenge of deploying large-scale models on hardware accelerators.
Design, develop, and test software for machine learning tools and frameworks.
Optimize machine learning models to be smaller and more efficient for edge devices.
Build solutions for next-generation mobile and embedded machine learning platforms.
Work with popular neural network frameworks like TensorFlow and PyTorch.
Develop AI systems infrastructure, including distributed inference platforms and compiler technologies.
Optimize performance for GPU acceleration and runtime systems.
Deploy large-scale machine learning workloads across various hardware accelerators.
What we're looking for
Must be currently enrolled in a bachelor’s, master’s, or Ph.D. degree program in computer engineering, computer science, electrical engineering, or a related field.
Must be available for 11–14 weeks during Summer 2027 (May–September).
Expected graduation date must be November 2027 or later.
Must have 1+ years of experience with programming languages such as C, C++, and Python.
Preferred to be enrolled in a Master’s or PhD degree program with an anticipated graduation between November 2027 and June 2028.
Proficiency in deep neural networks, machine learning algorithms, and architectures (CNNs, RNNs, LSTMs) (preferred).
Experience with ML frameworks like TensorFlow, TFLite, or PyTorch (preferred).
Machine Learning
Artificial Intelligence
Python
Java
C++
TensorFlow
PyTorch
MLFlow
NLP
Computer Vision
Information Retrieval
Data Engineering
Ranking
Content Recommendations
Production ML Systems
Machine Learning
Python
TensorFlow
PyTorch
scikit-learn
Data Pipelines
R
Java
APIs
Backend Services
Statistics
Probability
Fraud Detection
Risk Modeling
AI-assisted development tools
Claude Code
Machine Learning
Python
TensorFlow
PyTorch
scikit-learn
Data Pipelines
R
Java
APIs
Backend Services
Statistics
Probability
Fraud Detection
Risk Modeling
AI-assisted development tools
Claude Code