Software Engineering Intern

About Rivian

Rivian is on a mission to keep the world adventurous forever.

Role Summary

Rivian internships are experiences optimized for student candidates. To be eligible, you must be an undergraduate or graduate student in an accredited program during the internship term with an expected graduation date between December 2027 and 2029. Rivian's Internship Program requires active student enrollment. Our Spring Co-Op program runs from Jan - Aug, 2027. You must be available to work full time (40 hours per week), onsite, for the entire duration.

As a Machine Learning Research Intern on Rivian's Autonomy team, you will advance neural scene reconstruction and world modeling.

Key Responsibilities

• Research, prototype, and train 3D machine learning models for scene reconstruction, novel view synthesis, and neural rendering from multi-sensor driving data (camera, lidar, radar, and vehicle pose).
• Investigate and extend modern approaches such as neural radiance fields, 3D Gaussian splatting, feed-forward reconstruction, depth and geometry estimation, and generative/diffusion-based scene synthesis.
• Design quantitative and qualitative evaluation methodology for reconstruction and rendering quality, including photometric, geometric, and downstream task-driven metrics.
• Scale training and inference pipelines to large real-world datasets using distributed, multi-GPU infrastructure.
• Collaborate with perception, simulation, and infrastructure teams to integrate research prototypes into our internal research platform.
• Present results regularly to the team, contribute to internal technical documentation, and deliver a final presentation summarizing your research contribution.

Requirements & Tech Stack

• Must be currently pursuing a PhD degree at an accredited university.
• Actively pursuing a degree or one closely related in computer science, electrical engineering, statistics, or a closely related quantitative field.
• Research background in one or more of the following: 3D scene reconstruction, neural rendering / novel view synthesis, depth or geometry estimation, 3D scene understanding, or generative modeling.
• Track record of first-author publications at top-tier machine learning or computer vision conferences and journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, SIGGRAPH, CoRL, RSS).
• Strong proficiency in Python and a modern deep learning framework (PyTorch preferred), with demonstrated ability to implement, train, and debug models independently.
• Solid foundation in the mathematics underlying 3D vision: multi-view geometry, camera models, coordinate transforms, and 3D representations (point clouds, meshes, voxels, implicit fields).
• Experience applying 3D reconstruction, neural rendering, or world modeling to autonomous driving, robotics, or other embodied domains.
• Experience with large-scale distributed training and multi-GPU/multi-node workflows.
• Familiarity with simulation, sensor simulation, or synthetic data generation for training and evaluating ML systems.
• Experience working with large multi-sensor datasets and the calibration, synchronization, and data-quality challenges they involve.
• Open-source contributions, released research code, or a strong public portfolio of 3D ML work.

Pay Disclosure

The salary range for this role is $33.00-51.00/HR for Palo Alto based applicants.