We are now looking for a Machine Learning Intern. Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time — the era of AI has begun. Image recognition and speech recognition — GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. The GPU started out as the engine for simulating human creativity, conjuring up the amazing virtual worlds of video games and Hollywood films.
Now, NVIDIA's GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for Deep Learning, and NVIDIA is increasingly known as “the AI computing company.
What you’ll be doing:
- Support development of AI agents for workflow automation and intelligent assistants.
- Work on conversational AI, LLM applications, RAG systems, and speech AI workflows.
- Build prototypes and demos using NVIDIA AI technologies such as NeMo, NIM, ACE, Riva, or related tools.
- Assist with evaluation, testing, optimization, and deployment workflows.
- Collaborate with cross-functional teams on enterprise and industry AI use cases.
What we need to see:
- Pursuing BS, MS, or PhD in Computer Science, AI, Data Science, Engineering, or related fields.
- Experience with machine learning / deep learning.
- Strong Python programming skills.
- Familiarity with Linux environments.
- Good analytical and communication skills.
Ways to Stand Out from the Crowd
- Experience with NLP, LLM applications, agentic AI, speech AI, or system integration.
- Experience with APIs, cloud platforms, or model deployment.
- Interest in real-world AI product development.