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Relay

Relay Launchpad Intern - Software Engineer (AI/ML)

Raleigh, NCinternshipinternAdded today

About this role

Relay is seeking an AI/ML-focused software engineering intern for summer 2027 to help build voice-powered tools for frontline workers. You'll work full-time in-office in Raleigh, NC, contributing to product development, ML model implementation, and production integration across a diverse tech stack.

What you'll do

  • Collaborate with engineers on product features, enablement tools, and bug resolution
  • Design and evaluate machine learning models using supervised, unsupervised, and deep learning techniques
  • Preprocess and engineer datasets for improved model training efficiency
  • Integrate trained ML models into production systems with focus on scalability
  • Develop automated tests to ensure quality and reliability
  • Research and share best practices in AI/ML to elevate team standards

What they're looking for

  • Python programming
  • Machine learning frameworks (PyTorch or JAX)
  • ML model design and training
  • Data preprocessing and feature engineering
  • Linux systems
  • Agile software development
  • Problem-solving and communication
  • LLMs and applied AI/ML (desired)

Benefits

  • Paid internship with conversion potential to full-time role
  • Full-time in-office work in amenity-rich headquarters
  • Hands-on experience across diverse tech stack
  • Mentorship from experienced engineering team
  • Opportunity to build products impacting real-world users
  • Culture focused on impact, team, and learning
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Relay

Relay builds an AI-powered cloud platform designed to impact frontline workers through innovative digital solutions. The company is hiring for technical roles including Solutions Engineers, Software Engineers (for Site Reliability Engineering and Embedded Development), and QA Engineers across various experience levels.

View all jobs at Relay

Likely interview questions

  • Walk us through a machine learning project you've completed—what problem did you solve, and how did you evaluate your model?
  • How have you approached data preprocessing and feature engineering in past projects?
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