Applied ML Engineering Student

NVIDIA is looking for a Machine Learning Engineer Intern to join our team. As a Machine Learning Engineer Intern, you will collaborate closely with Data Scientists and ML Researchers to develop innovative ML solutions and proof-of-concepts. This position allows you to make a real impact in a dynamic, technology-focused company by building user-friendly web interfaces for ML applications, managing cloud-based infrastructure, and ensuring the performance and accuracy of ML models in production.

What You'll Be Doing

• Collaborating with Data Scientists and ML Researchers to understand project requirements and objectives.
• Designing and implementing web interfaces for ML applications using frameworks like Streamlit.
• Developing and maintaining backend services in Python for data processing, model inference, and integration into application workflows.
• Setting up and managing cloud-based infrastructure for ML models, including scheduling automated inference and retraining jobs.
• Implementing performance monitoring systems for ML models in production.
• Documenting the development process, system architecture, and user guides for technical teams and end-users.
• Participating in team meetings and brainstorming sessions to improve project outcomes.
  
  

• Enrollment in a Bachelor’s or Master’s program in Computer Science, Engineering, Data Science, Machine Learning, or a related field.
• Strong programming skills in Python, with experience in web development frameworks preferred.
• Familiarity with ML concepts and experience using ML libraries like scikit-learn, TensorFlow, PyTorch.
• Experience with cloud computing services (AWS, Google Cloud, Azure) and knowledge of Docker, Kubernetes is a plus.
• Ability to work effectively in a team, with strong communication skills and a commitment to achieving project goals.
• Self-motivated learner with a passion for technology.
  
  

• Demonstrated experience in developing and deploying ML applications.
• Knowledge of advanced ML techniques and algorithms.
• Experience with performance optimization of ML models in a cloud environment.