Level

NVIDIA

Applied Deep Learning Algorithm Intern, Autonomous Driving - 2027

NVIDIA is hiring an Applied Deep Learning Algorithm Intern, Autonomous Driving - 2027 in Shanghai, China. Level rates it ; you can apply on Level.

AI in this role

pytorch
fine-tuningai-evaluation

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.

How we rate this

Applied Deep Learning Algorithm Intern, Autonomous Driving - 2027 at NVIDIA rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningAI EvaluationPyTorch

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. How do you decide that one model's output is better than another's for a given task?
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, AI Evaluation, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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