About the job
Avride develops autonomous vehicles and delivery robots across the United States, building and operating both autonomous cars and delivery robots with shared core technologies. The company focuses on advancing both fields together through integrated research and engineering.
Internship overview
The Machine Learning Engineer Internship in Austin, TX offers hands-on work with real driving data and exposure to advanced academic research. Interns join the Perception team, which interprets raw sensor data, including cameras, LiDAR, and microphones, to construct a detailed, real-time 3D view of the environment for autonomous vehicles.
Each intern works directly with a senior mentor and contributes to projects that influence the safety and performance of Avride’s systems. The internship bridges theory and practice: interns help turn new concepts and system architectures into working prototypes, then assess their effectiveness in complex, safety-critical scenarios.
What you will do
Four distinct internships are available within the Perception team for Summer 2026. Projects focus on 3D perception and sensor fusion challenges in autonomous driving.
Long-Tail 3D Entity Recognition via Pre-Trained 2D Models
- Lead efforts to address long-tail entity recognition, a persistent challenge in autonomous driving, by exploring how pre-trained, open-source 2D models can enhance 3D recognition tasks.
- Design and run simulation-based experiments to test whether models can identify rare or infrequent objects while maintaining precision.
- Work closely with a mentor to prototype and refine methods for integrating 2D model features into Avride’s perception stack.
- Present findings, including recall and precision trade-offs, and simulation methodologies, to Avride’s research and engineering teams at the conclusion of the internship.
RGB-Only 3D Perception & RGB-LiDAR Fusion
- Take ownership of a targeted research project aimed at improving 3D perception by reviewing current literature on RGB-only approaches and developing hypotheses to advance sensor fusion techniques.

