Nvidia Internships Phd Generative Ai Research jobs at Nvidia

What you'll be doing:

• Develop new Deep Learning models for automatic speech recognition, speech synthesis, neural machine translation and natural language

• Design new large scale training algorithm

• Open-source models using NeMo conversational AI frameworks

• Mentor interns

• Publish research papers on top speech and NLP conferences

• Collaborate with universities and research teams.

What we need to see:

• PhD in Computer Science or Electrical Engineering (or equivalent experience)

• Proven understanding of Deep Learning for Natural Language Processing or Speech Recognition

• At least 5 years of research experience in speech recognition or NLP

• Excellent Python programming skills

• Experience with PyTorch

• Strong publications record

Ways to stand out from the crowd:

• Contribution to open-source projects

• Being reviewers for one of the top speech conferences

What you'll be doing:

• Lead the commissioning, bring-up, and operational readiness of new data centers.

• Collaborate with software and hardware teams to define and implement repeatable procedures.

• Own the operations, maintenance, and reliability of the infrastructure of an AI datacenter.

• Develop and enforce operations strategy & processes, ensuring strict adherence to SLAs across critically important infrastructure.

• Define and implement procedures for minimal downtime and quality controls to strive to achieve continuous uptime.

• Feeding requirements to software and hardware teams

• Creation of documentation that the ecosystem can use to run their own AI Data Centers

What we need to see:

• BS, MS degree in Computer Engineering/Science, or related field (or equivalent experience) with 15+ overall years of relevant work experience and 8+ years of management experience.

• 8+ years of expertise in managing extensive data center operations or critical infrastructure.

• Expertise in BMS & Power management.

• Experience building 24/7 teams from 0

• Experience working with remote hands

• Proven track record of managing infrastructure from deployment through long-term operations.

• Experience driving reliability with robust processes, rapid field response, and recovery.

As a Research Scientist specializing in Generative AI for Physical AI, you'll be at the forefront of developing next-generation algorithms that bridge the gap between virtual and physical realms. You'll work with state-of-the-art technology and have access to massive computational resources to bring your ideas to life.

What you'll be doing:

• Pioneer revolutionary generative AI algorithms for physical AI applications, with a focus on advanced video generative models and video-language models

• Architect and implement sophisticated data processing pipelines that produce premium-quality training data for Generative AI and Physical AI systems

• Design and develop cutting-edge physics simulation algorithms that enhance Physical AI training

• Scale and optimize large-scale training systems to efficiently harness the power of 20,000+ GPUs for training foundation models

• Author influential research papers to share your groundbreaking discoveries with the global AI community

• Drive innovation through close collaboration with research teams, diverse internal product groups, and external researchers

• Build lasting impact by facilitating technology transfer and contributing to open-source initiatives

What we need to see:

• PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).

• Deep expertise in PyTorch and related libraries for Generative AI and Physical AI development

• Strong foundation indiffusion, vision language and reasoning models and their applications

• Proven experience with reinforcement learning algorithms and implementations

• Robust knowledge of physics simulation and its integration with AI systems

• Demonstrated proficiency in 3D generative models and their applications

Ways to stand out from the crowd:

• Publications or contributions to major AI conferences (ICLR, NeurIPS, ICML, CVPR, ECCV, SIGGRAPH, ICCV, etc.)

• Experience with large-scale distributed training systems

• Background in robotics or physical systems

• Open-source contributions to prominent AI projects

• History of successful research-to-product transitions

What you'll be doing:

• Design, build and optimize agentic AI systems for the CUDA ecosystem.

• Co-design agentic system solutions with software, hardware and algorithm teams; influence and adopt new capabilities as they become available.

• Develop reproducible, high-fidelity evaluation frameworks covering performance, quality and developer productivity.

• Collaborate across the AI stack—from hardware throughcompilers/toolchains,kernels/libraries, frameworks, distributed training, andinference/serving—andwith model/agent teams.

What we need to see:

• Bachelor’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); MS or PhD preferred.

• 5+ years of industry or academia experience with AI systems development; exposure to building foundational models, agents or orchestration frameworks; hands-on experience with deep learning frameworks and modern inference stacks.

• Strong C/C++ and Python programming skills; solid software engineering fundamentals.

• Experience with GPU programming and performance optimization (CUDA or equivalent).

Ways To Stand Out From The Crowd:

• Track record building/evaluating deep learning models, coding agents and developer tooling.

• Demonstrated ability to optimize and deploy high-performance models, including on resource-constrained platforms.

• Deep expertise in GPU performance optimizations, evidenced by benchmark wins or published results.

• Publications or open-source leadership in deep learning, multi-agent systems, reinforcement learning, or AI systems; contributions to widely used repos or standards.

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What you’ll be doing:

• Decompose research questions into smaller, more manageable parts and tackle them in iterative steps.

• Think critically to identify unseen gaps, and creatively bridge them with non-traditional, high-impact solutions.

• Connect with researchers and product engineers to ground research findings in real-world problems.

• Lead knowledge dissemination effort, with options for conference, journal, and in-house publication.

What we need to see:

• Currently pursuing a Ph.D. in the field of Computer Science/Engineering, Electrical Engineering, or related fields.

• Experience in gaming and/or human behavior in related application domains, demonstrated by one or more lead-author publications.

• Examples of public portfolios (e.g. repositories, OSS contributions, notebooks, packages, or technical blog posts with code).

• Proficiency with Python, Rust, and/or C++.

• Experience with AI model training and evaluation frameworks, like PyTorch.

• Comfortable with modern software development and version-control systems (e.g., GitHub/lab).

• Familiarity with and interest in modern deep learning models, including working with large-scale, multi-modal foundation models (such as recent LLMs, VLMs).

• Strong understanding of deep learning fundamentals and recent trends.

Ways to stand out from the crowd:

• Experience with multi-node, multi-GPU training and inference workflows.

• Proficiency in crafting, implementing, and conducting behavioral experiments involving human subjects and interactive visual stimuli.

• Experience with quantitatively modeling human perception, movement, and decision-making.

• A track record of applying human behavior models to machine learning applications.

You will also be eligible for Intern

What you will be doing:

• Building an end-to-end agentic AI applications that solve real-world enterprise problems across various industries.

• Serve as the primary technical domain expert for pre- and post-sale for partners, embedding deeply with them to design and deploy Generative AI solutions at scale. Maintain strong relationships with leadership and technical teams to drive adoption, and successful utilization of NVIDIA GenAI platforms.

• Accelerate partner/customer time to value by providing repeatable reference architecture guidance, building hands-on prototypes, and advising on standard methodologies for scaling solutions to productions.

• Establish the scope, success metrics, and evaluation criteria for partner-led customer projects, ensuring alignment to standardized and reproducible GPU-accelerated workflows.

• Enable strategic partners to build their own Professional Services, platforms and products by integrating and accelerating using NVIDIA technologies for high-impact customer workloads. You will proactively find opportunities to drive deeper adoption and utilization of NVIDIA's Generative AI products.

• Codify knowledge and operationalize technical success practices to help partners scale impact across industries and workloads.

What we need to see:

• MS or PhD degree in Computer Science/Engineering, Machine Learning, Data Science, Electrical Engineering or a closely related field (or equivalent experience).

• 5+ years of meaningful work experience in deploying AI models at scale as a Software Engineer or Deep Learning engineer.

• Consistent track record of building enterprise-grade agentic AI systems using open-source models and solid foundation in deep learning, with a particular emphasis on LLM and VLM.

• Hands-on experience with LLM and agentic frameworks (NeMo Agent Toolkit, LangChain, Semantic Kernel, Crew.ai, AutoGen) and evaluation and observability platforms. Comfortable building prototypes or proofs of concept

• Strong coding development and proficiency in Python, C++ and Deep Learning frameworks (PyTorch, or TensorFlow).

• Excellent communication and presentation skills to effectively collaborate with both internal executives, partners and customers.

Ways to stand out from the crowd:

• Demonstrate expertise in building applications and systems using NeMo Framework, Nemotron, Dynamo, TensorRTLLM, NIMs, AI Blueprints. And actively contribute to the open-source community.

• Take end-to-end ownership of projects, proactively acquiring new skills or knowledge as needed to drive success.

• Excel in fast-paced environments, adeptly managing multiple workstreams and prioritizing for the highest customer impact.

• Understanding of different advanced agent architectures and emerging communication protocols (MCP, OpenAI Agentic SDK, or Google A2A).

• NVIDIA GPUs and system software stacks (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, MPI, NVLink and others.

What you'll be doing:

• Leading the end-to-end product lifecycle, from new features to supporting new AI platforms, delivering multiple releases per year.

• Collaborating with cross-functional teams, including engineering, marketing, and sales, to successfully implement product strategies and roadmaps.

• Writing clear requirements, user stories, and compelling user experiences to ensure a quality product.

• Managing release schedules and coordinating with development teams to ensure timely delivery of enterprise software products.

• Applying sophisticated product management software to monitor progress, track metrics, and report on the success of product initiatives.

What we need to see:

• Bachelor's degree in Computer Science, Engineering, or equivalent experience.

• Minimum of 8 years of experience in software product management.

• Extensive hands-on experience with compute, network, and storage technologies.

• Proven proficiency in release management strategies and adept utilization of product management software tools.

• Proven written and verbal communication skills. Ability to effectively connect with technical and non-technical stakeholders.

• Leadership skills! Remove obstacles. Resolve ambiguity. Comfortable presenting and defending your fact-based opinion or recommendation.

Ways to stand out from the crowd:

• Hands-on experience with NVIDIA Base Command Manager or Bright Cluster Manager

• Experience as an SRE, datacenter operator, infrastructure manager

• Experience with high-performance computing

• Background with Software Development Life Cycle