NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern deep learning — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.“ We're looking to grow our company and establish teams with the smartest people in the world.
GPU computing is the most productive and pervasive platform for deep learning and AI. It begins with the most advanced GPUs and the systems and software we build on top of them. We integrate and optimize every deep learning framework. We work with most major technology providers and support a broad range of Fortune 500 companies in their machine and deep learning needs.
With deep learning, we can teach AI to do almost anything. New internet services, like Google Assistant, have learned speech from sound and provide a more natural way to access information. Self-driving cars use deep learning to recognize the space the car inhabits, the lanes in which it drives, and the objects to avoid. In healthcare, neural networks trained with millions of medical images can find clues in MRIs that until now could only be found through invasive biopsies. In recommendation systems, we learn how to understand users' desires and serve them what they truly are looking for. These are just a few examples. AI will spur a wave of social progress unmatched since the industrial revolution.
Machine Learning Intern, Recommender Systems.
NVIDIA is seeking a Machine Learning Intern to work on the next generation of recommendation algorithms and exploring the limitations of accelerating training and inference on GPU. You'll join a team of ML Engineers, Applied Research Scientists, HPC Engineers and Software Engineers developing a platform designed to make the productionization of GPU-based recommender systems as simple as possible. Join us and help shape the future of recommender systems.
What you'll be doing:
- Building upon tools like RAPIDS, TRTIS, PyTorch, the Tensorflow ecosystem and others to create example workflows that demonstrate the feasibility and effectiveness of GPU-based recommenders for feature engineering, data loading, training and inference.
- Hands on practice of ML using machine learning libraries and frameworks such as scikit-learn, XGBoost, Pytorch and Tensorflow.
- Identifying, Profiling, and understanding bottlenecks and performance issues at every stage of the recommendations pipeline from ETL to Inference.
- Providing expertise regarding the deployment of RecSys models in production.
- Working with HPC Engineers to identify and fix bottlenecks in GPU-based RecSys workflows.
- Working with Applied Scientists to bring the most advanced recommender systems technology into our RecSys platform, integrating models into workflows and scaling them to production capability.
- Coordinating with Software Engineers to help package these workflows into a standardized library that enables fast and easy deployment of recommender systems.
What we need to see:
- Pursuing an MS or PhD in Computer Science, Engineering, Physics or other relevant field with a focus in machine learning, high performance computing, or systems.
- 2+ years of experience as a systems engineer, preferably building and maintaining distributed systems.
- 1+ years experience deploying recommender systems into production at scale across a range of models and platforms.
- First hand experience with deploying and maintaining ML systems and services in production at scale.
- Experience with feature engineering and storing (Spark, dataflow, …).
- An understanding of CUDA, numba, PyTorch JIT, and other optimization techniques
- An ability to share and communicate your ideas clearly through blog posts, kernels, GitHub, etc.
- Excellent communication and interpersonal skills are required, along with the ability to work in a dynamic, product oriented, global team.
NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most brilliant and talented people in the world working for us. Are you creative and autonomous? Do you love a challenge? If so, we want to hear from you.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression , sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.