Deep Learning Compiler Intern Summer jobs at Nvidia

What you'll be doing:

• Research, design and implement novel methods for efficient deep learning.

• Publish original research.

• Collaborate with other team members and teams.

• Mentor interns.

• Speak at conferences and events.

• Work with product groups to transfer technology.

• Collaborate with external researchers.

What we need to see:

• Completing or recently completed a Ph.D. in Computer Science/Engineering, Electrical Engineering, etc., or have equivalent research experience.

• Excellent knowledge of theory and practice of computer vision methods, as well as deep learning.

• Background in pruning, quantization, NAS, efficient backbones, and so on, is a plus.

• Experience with large language models and large vision-language models is required.

• Excellent programming skills in Python and PyTorch; C++ and parallel programming (e.g., CUDA) is a plus.

• Hands-on experience with large-scale model training including data preparation and model parallelization (tensor and pipeline) is required.

• Outstanding research track record.

• Excellent communications skills.

In this critical role, you will manage a team to expand NeMo Framework's capabilities, enabling users to develop, train, and optimize models by designing and implementing the latest in distributed training algorithms, model parallel paradigms, model optimizations, defining robust APIs, meticulously analyzing and tuning performance, and expanding our toolkits and libraries to be more comprehensive and coherent. You will collaborate with internal partners, users, and members of the open source community to analyze, design, and implement highly optimized solutions.

What you’ll be doing:

• Plan, schedule, mentor, and lead the execution of projects and activities of the team.

• Collaborate with internal customers to align priorities across business units.

• Coordinate projects across different geographic locations.

• Grow and develop a world-class team.

• Contribute and advance open source

• Solve large-scale, end-to-end AI training challenges, spanning the full model lifecycle from initial orchestration, data pre-processing, running of model training and tuning, to model deployment.

• Work at the intersection ofcomputer-architecture,libraries, frameworks, AI applications and the entire software stack.

• Innovate and improve model architectures, distributed training algorithms, and model parallel paradigms.

What we need to see:

• Excellent understanding of SDLC practices including architecting, testing, continuous integration, and documentation

• MS, PhD or equivalent experience in Computer Science, AI, Applied Math, or related field

• 8+ overall years of industry experience, including 3+ years of management experience.

• Proven experience to lead and scale high-performing engineering teams, especially across distributed and functional groups.

• Experience with AI Frameworks (e.g. PyTorch, JAX), and/or inference and deployment environments (e.g. TRTLLM, vLLM, SGLang).

• Proficient in Python programming, software design, debugging, performance analysis, test design and documentation.

• Consistent record of working effectively across multiple engineering initiatives and improving AI libraries with new innovations.

Ways to stand out from the crowd:

• Hands-on experience in large-scale AI training, with a deep understanding of core compute system concepts (such as latency/throughput bottlenecks, pipelining, and multiprocessing) and demonstrated excellence in related performance analysis and tuning.

• Expertise in distributed computing, model parallelism, and mixed precision training.

• Prior experience with Generative AI techniques applied to LLM and Multi-Modal learning (Text, Image, and Video).

• Knowledge of GPU/CPU architecture and related numerical software.

• Created / contributed to open source deep learning frameworks.

What you will be doing:

• Design and build PyTorch components that run efficiently on supercomputers with 1000s-100ks of GPUs.

• Collaborate with NVIDIA’s hardware and software teams to improve the overall GPU performance in PyTorch.

• Design, build and support production AI solutions used by enterprise customers and partners.

• Work with internal applied researchers to improve their AI tools.

What we need to see:

• BS in Computer Science or Engineering (or equivalent experience).

• 3+ years professional experience in deep learning.

• Proficient with C++ programming.

• Strong understanding of systems software and interfaces.

• Demonstrated experience with Thread and Distributed Parallel Programming

• Demonstrated background developing large software projects.

• Strong verbal and written communication skills

Ways to stand out from the crowd:

• Contributions and participation in the open source community.

• Familiarity with deep learning compilers.

• Familiarity with deep learning modeling trends.

• Background with CUDA Programming as well as Python.

• Demonstrated experience working with multi-disciplinary teams.

What you’ll be doing:

• Understand, analyze, profile, and optimize AI training workloads on state-of-the-art hardware and software platforms.

• Guide development of future generations of artificial intelligence accelerators and systems.

• Develop detailed performance models and simulator infrastructure for computing systems accelerating AI training, and implement and evaluate hardware feature proposals.

• Collaborate across the company to guide the direction of machine learning at NVIDIA; spanning teams from hardware to software and research to production.

• Drive HW/SW co-design of NVIDIA’s full deep learning platform stack, from silicon to DL frameworks.

What we need to see:

• PhD in CS, EE or CSEE and 3+ years; or MS (or equivalent experience) and 6+ years of relevant work experience.

• Strong background in computer architecture, with a proven track record of architecting features in shipping high-performance processors.

• Background in artificial intelligence and large language models, in particular training algorithms and workloads.

• Experience analyzing and tuning application performance on state-of-the-art hardware.

• Experience with processor and system-level performance modelling, simulation, and evaluation before silicon exists.

• Programming skills in C++ and Python.

• Familiarity with GPU computing across all layers of the AI stack, from DL frameworks like PyTorch down to CUDA.

What you’ll be doing:

• Develop and test sample applications for chemistry and materials discovery using artificial intelligence.

• Help develop AI first workflows using NVIDIA technology and popular deep learning frameworks.

• Create clear, practical examples and documentation for developers and researchers.

What we need to see:

• Pursuing a PhD in Chemistry, Materials Science, Computer Science, or a related field.

• Familiarity with AI/ML concepts and experience with at least one deep learning framework (e.g., PyTorch, TensorFlow).

• Basic understanding of chemistry or materials science principles.

Ways to stand out from the crowd:

• Experience with GPU programming or CUDA and machine learning frameworks such as PyTorch.

• Contributions to open-source projects related to AI or scientific computing.

• Coursework or projects involving AI for scientific applications.

You will also be eligible for Intern

What you'll be doing:

• Develop and refine deep learning models and techniques for genomics analysis, including but not limited to DNA sequencing, variant calling, and model prediction.

• Advance and apply modern Deep Learning techniques to develop Large Language Models (LLMs), Graph Neural Networks, Graph Transformer Networks, and comprehensive multi-modal models in genomics.

• Design and implement machine learning techniques to tailor foundation models for downstream genomic specific tasks.

• Generate and manage datasets for large-scale machine learning, focusing on learning from genomics specific applications.

• Collaborate closely with product and hardware architecture teams to ensure flawless integration of research and development into NVIDIA products.

• Work in tandem with engineering and AI research teams to employ the latest technologies for scalable and innovative genomics analysis.

What we need to see:

• Master or Ph.D. in Computer Science, Bioinformatics, or Computational Biology, or related field (or equivalent experience).

• 3+ years of experience in related field.

• Proficiency in C/C++ and Python, with a strong grasp of software design and programming principles.

• Background with Large Language Models (LLMs) and natural language processing (NLP), Generative AI and Foundation Models.

• Strong proficiency with modern frameworks such as PyTorch and TensorFlow, Experience with Large scale inferencing.

• Experience in building and implementing complex algorithms and data structures, with a focus on bioinformatics or genomics applications.

• Deep understanding of computer system architecture, operating systems, and the challenges associated with large-scale genomic data analysis.

Ways to stand out from the Crowd:

• Hands-on experience in using LLM, Graph Neural Network, Graph Transformer Network particularly those applied to genomics data.

• Strong collaborative and interpersonal skills to effectively work and influence within a dynamic, technical environment.

• Ability to decompose complex requirements into step by step tasks and reuse available solutions to implement most of those.

What you’ll be doing:

• Develop methods, execute performance tests, and analyze data for IC components and board-level product certifications.

• Participate in research and development of novel IC qualification processes.

• Review and interpret equipment manuals, schematics, and technical documents to support equipment setup, calibration, and installation.

• Assist engineers with analysis tasks, including equipment setup, sample preparation, basic thermal/power characterization, and troubleshooting of test equipment.

• Support reliability lab operations by contributing to test requirement assessments, metrology design/optimization, and mechanical fixture assembly/debug.

• Prepare and deliver project updates and lessons learned to the team.

What we need to see:

• Currently pursuing a masters degree in Materials Science, Mechanical Engineering, or a related field.

• Knowledge with IC packaging, reliability testing, and semiconductor technologies.

• Knowledge withpackaging/test/failureanalysis lab equipment is a plus.

• Knowledge of CAD/SolidWorks or similar design software is desirable.

• Strong problem-solving skills, attention to detail, and ability to work both independently and collaboratively.

• Excellent communication skills, with the ability to document and present technical findings clearly.

Ways to stand out from the crowd:

• Knowledge in IC packaging and physics of failure

• Experience in high power testing, such as Hipot, SIR, ECM

• Experience in image processing

You will also be eligible for Intern