Applied Deep Learning Algorithm Intern, Autonomous Driving - 2027
At a glance
Junior AI Research role at NVIDIA. China, Shanghai · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and assess learning-based methods for autonomous-driving decisions, planning and driving behavior
- Test Transformer models, including VLMs and VLAs, on scene interpretation, driving choices and trajectory generation
- Create Python workflows for supervised fine-tuning data, from scenario mining and cleanup to labeling and model assessment
- Inspect vehicle logs and model errors, then mine difficult cases and improve dataset balance, long-tail coverage and noise
- Run fine-tuning, reinforcement-learning and evaluation studies using offline measures, replay, simulation and vehicle-test results
What you bring
- You are pursuing a master's or PhD in computer science, AI, robotics, mathematics or a related discipline
- You program well in Python and have applied PyTorch or another deep-learning framework
- You understand machine learning, deep learning, Transformers, model training and optimization, and dataset analysis
- You communicate effectively in spoken and written English and can work at least three days each week
Who this fits
This internship suits a graduate student interested in applied deep learning for autonomous driving. You'll collaborate with experienced engineers and global teams, and the schedule requires at least three workdays per week.
From the employer
Are you looking to be part of a fun, creative company at the forefront of groundbreaking technologies? NVIDIA is leading the way in some of the world's most exciting tech areas, including Virtual Reality, Artificial Intelligence, Deep Learning, and Autonomous Vehicles. We are widely recognized as one of the most desirable employers, with some of the most forward-thinking individuals in the industry working with us. There has never been a more exciting time to join our team!
We are looking for a highly motivated Applied Deep Learning Algorithm Intern to join our autonomous driving team. You will work with experienced engineers on learning-based driving algorithms, supervised fine-tuning, reinforcement learning, data optimization, model evaluation, and multi-carline adaptation.
What You Will Be Doing:
- Develop, train, fine-tune, and evaluate learning-based algorithms for autonomous driving decision-making, planning, and driving behavior.
- Explore Transformer-based approaches, including VLMs and VLAs, for driving-scene understanding, decision-making, and trajectory generation.
- Build supervised fine-tuning data workflows and Python tools for scenario mining, data cleaning, filtering, labeling, experiment management, visualization, and model evaluation.
- Analyze model failures and vehicle logs, mine hard cases, and improve dataset balance, long-tail coverage, and noise quality.
- Conduct supervised fine-tuning, reinforcement learning, ablation, and evaluation experiments using offline metrics, open-loop replay, simulation, and vehicle-test results.
- Support model and carline adaptation across multiple autonomous driving programs, including issue analysis, validation, and scaling.
What We Need to See:
- Currently pursuing a Master's or PhD in Computer Science, Artificial Intelligence, Robotics, Mathematics, or a related field.
- Good Python programming skills and hands-on experience with PyTorch or another deep learning framework.
- Good understanding of machine learning, deep learning, Transformer architectures, model training, optimization, and dataset analysis.
- Proficient English communication skills, both written and verbal, with the ability to collaborate effectively with global teams.
- Able to work at least three days per week throughout the internship.
Ways to Stand Out from the Crowd:
- Experience with supervised fine-tuning, imitation learning, reinforcement learning, behavior cloning, or learning-based planning.
- Experience using or fine-tuning VLMs, VLAs, or other multimodal foundation models.
- Familiarity with autonomous driving datasets, trajectory data, vehicle logs, simulation, or scenario-mining pipelines.
- Strong C++ skills or relevant research, open-source, publication, or competition experience.