Software Engineering Intern, Mapping Autonomous Vehicles - Fall 2026

Nvidia

Quick summary

Work type
On-site
Location
Santa Clara, CA
Employment
Intern
Posted
1 day ago

Market check

Salary context

How this pay compares to similar roles

Similar $201k
$157k most similar roles pay here $251k

This listing doesn't post a salary. Most similar roles pay $166,500–$235,750.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 985 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 971 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Software Engineering Intern, Mapping Autonomous Vehicles - Fall 2026

Apply Now Log in to save

Join our dynamic map team as a software engineer intern at NVIDIA, where you will work closely with senior engineers to build high-quality, scalable HD maps for autonomous vehicles using cutting-edge technologies like Kubernetes, Docker, AWS, and GPU technology. Your day-to-day responsibilities include developing automation algorithms, data pipelines, and visualization tools to enhance mapping efficiency and quality while exploring innovative ways to integrate state-of-the-art generative AI techniques with our extensive dataset. You will collaborate extensively with various teams involved in perception, infrastructure, simulation, and more within the autonomous vehicle ecosystem. Ideal candidates are pursuing a degree in Computer Science or related fields with strong programming skills in Python, C++, or Java, along with experience in robotics, automotive systems, deep learning, computer vision, SLAM, sensor fusion, and cloud infrastructure.

Skills

Python C++ Java Kubernetes Docker AWS Deep Learning Computer Vision SLAM Sensor Fusion GPU Technology CI/CD

What you'll do

  • Build automation algorithms to enhance map building quality and efficiency.
  • Develop data pipelines for scalable HD mapping solutions.
  • Create visualization tools to support new mapping features and improvements.
  • Collaborate with senior engineers to understand and contribute to the tech stack.
  • Explore innovative AI technologies to address challenges in map building and QA.
  • Engage with various teams using map data to ensure alignment and support.

What we're looking for

  • Pursuing a BS, MS, or PhD in Computer Science, Engineering, or related fields
  • Strong programming skills in Python, C++, or Java
  • Experience with cloud infrastructure (Kubernetes, Docker, AWS)
  • Background in robotics, automotive technology, or AI
  • Knowledge of AV technologies: self-driving cars, deep learning, computer vision
  • Familiarity with SLAM, sensor fusion, and GPU technology
  • Engagement with map data consumers for perception, infrastructure, simulation

Related searches

More Software Engineering Intern jobs Software Engineering Intern jobs in Santa Clara All jobs at Nvidia

More like this

Similar roles

Senior Mapping Engineer - Autonomous Vehicles

Nvidia

Santa Clara, CA 54 days ago $184,000–$287,500
C++ Python NVIDIA Orin NVIDIA Xavier QNX Protocol Buffers Airflow Docker Kubernetes SD/HD mapping 3D geometry point clouds structure-from-motion real-time systems embedded software memory optimization cache optimization lock-free algorithms deterministic performance map data formats
Save

Senior Software Systems Engineer - Autonomous Vehicles

Nvidia

Remote (Santa Clara, CA) 31 days ago $152,000–$241,500
Python Magic_Cyber_Systems_Engineer Cameo Model-Based_Systems_Engineering V-Model ISO_26262 Robotics_Development Sensing Perception Motion_Control Systems_Integration Test_Strategy_Development Data_Analysis AI_Tooling
Remote
Save

Senior Research Engineer - Autonomous Vehicles

Nvidia

Santa Clara, CA 148 days ago $184,000–$287,500
PyTorch TensorFlow JAX Python C++ CUDA Kubernetes SLURM Reinforcement_Learning PPO SAC Q-learning GPU_Cluster HPC Distributed_Training_Systems Multimodal_Datasets Simulation_Infrastructure LLMs Policy_Learning Curriculum_Learning Domain_Randomization Reward_Shaping
Save