Research Scientist Efficient Deep Learning - New College Grad jobs at Nvidia

What you’ll be doing:

• Act as a technical advisor, collaborating with partner engineering teams on architecture, integration, and code for Isaac Sim, Isaac Lab, and AI-enabled robotics solutions.

• Build and maintain deep expertise in NVIDIA robotics SDKs, with a focus on simulation and robot learning.

• Track emerging trends in the robotics ecosystem to identify new opportunities.

• Co-design and deliver advanced robotic solutions with partners—defining objectives, architecture, milestones, and implementation plans.

• Create technical enablement resources such as sample code, reference architectures, integration guides, and workshops.

• Work closely with partner engineering leaders and decision-makers to identify challenges, recommend solutions, and drive adoption of NVIDIA robotic technology.

• Represent partner needs internally, providing feedback to NVIDIA’s product and engineering teams to influence future roadmaps.

• Support product launches and go-to-market activities with technical validation, demos, and customer-facing materials.

What we need to see:

• 10+ years in the technology industry, with at least 5 years in hands-on software development or engineering.

• Bachelor’s or Master’s in Computer Science, Engineering, or a related technical field—or equivalent experience.

• Whole software development lifecycle experience—from requirements and design to testing, integration, and support.

• Technical background in at least one of: robot foundation models, simulation, synthetic data, robot perception, reinforcement learning, or imitation learning.

• Expertise with robotics simulation tools (e.g., Gazebo, MuJoCo, Isaac Sim, Robot Studio).

• Experience leading technical workshops, code reviews, and architectural design sessions.

• Strong communication skills for technical and executive audiences.

• Ability to structure and deliver complex technical engagements.

Ways to stand out from the crowd:

• Experience with NVIDIA tools and libraries (CUDA-X, Isaac Sim, OpenUSD, Cosmos).

• Recent hands-on experience with robot simulation tools.

• Knowledge of AIOps, cloud-native technologies, Kubernetes, Docker, and monitoring tools.

• Proven success growing developer ecosystems through technical enablement.

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.

alignment, principledapproaches to synthetic data generation and filtering, advanced reasoning and inference algorithms for LLMs, novel learning paradigms and LLM architectures, and scientific understanding about the fundamental limitsand capabilitiesof LLMs. You will work within an amazing and collaborative research team that consistently publishes at the top venues in machine learning andnatural language

What you'll be doing:

• Explorealternative avenuesto unlock new capabilities in language models, including advanced knowledge acquisition techniques and innovative learning and decoding algorithms.

• Innovate newlearning paradigmsthat incorporate agency into the training of language models, such as enabling self-reflection and targeted knowledge enhancement.

• Enable learningfrom multi-modalitiesbeyond written text, such as acquiring physical commonsense knowledge through interactions with real-world environments.

• 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:

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

• At least 6 years of research experience (demonstrated by publication records spanning across 5+ years) in artificial intelligence, machine learning, natural language processing, computer vision or related subjects

• A history ofresearch successexemplified by a strong publication record and awards.

• Excellent knowledge of theory and practice of deep learning and natural language processing.

• Background in LLM training, alignment, and evaluation is expected.

• Excellent programming skills in Python and PyTorch.

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

• Excellent communications skills.

What you’ll be doing:

• Develop innovative parallel computing technologies that equip programmers with the compilers, languages, runtime systems, tools, and algorithms needed to solve challenges with modern accelerated systems.

• Collaborate with other researchers and engineers to extend the state of the art in parallel computing, machine learning, data analytics, and other technology areas surrounding NVIDIA's business. Deliver your innovations in high-quality software systems, publications, and patents.

• Engage with the research community through collaborations, publications, and presentations that produce new technologies and promote education in accelerated computing.

What we need to see:

• Completing or recently completed a Doctoral degree (Ph.D.) or equivalent experience in a computational field such as computer science, computer engineering, or scientific computing.

• Creativity in developing innovative solutions to the problems faced by parallel programmers and the skill to implement them in software.

• Expertise in parallel programming and algorithmic techniques.

• Strong programming ability in one or more of the following languages: C, C++, Rust and Python.

• Track record of research excellence and publications that demonstrate your body of work.

• Relevant research and software development experience.

Ways to stand out from the crowd:

• Expertise in applying programming system insights and techniques to problems in machine learning, data science, and distributed computing.

• Ability to implement ideas in the CUDA programming model.

• Experience with applying AI, such as large language models, to create new ways of solving the problems of software engineering.

• Prior success in building software systems used by other developers to solve their own problems.

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.

Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world!

We are now looking for an Low Power Design/Verification ASIC Engineer - New College Grad 2026. We continue to rapidly grow the research and development of energy-efficient GPU and SOC architectures. We are continually innovating in creative and unrivaled ways to improve our ability to deliver exceptional perf/watt solutions in a wide range of sectors. Come join NVIDIAs Low Power DV team to develop state of the art GPUs to power AI, Automotive, GeForce, and Mobile products.

What you'll be doing:

• Working very closely with Low Power Architecture, Design, and Software teams to understand next generation features.

• Responsible forarchitecting/developingtestbench, infrastructure, and testplans to verify various power management solutions for NVIDIA products.

• You will have the opportunity bring creative ideas that help improve power-aware DV methodologies, as well as influence EDA vendors to improve our simulation and debug efficiencies.

What we need to see:

• Pursuing or recently completed a BS, MS or PhD in Electrical or Computer Engineering,or equivalent experience.

• You have an understanding of low power design techniques such as multi VT, Clock gating, Power gating, Block Activity Power, and Dynamic Voltage-Frequency Scaling (DVFS) etc.

• Good understanding of processor architecture (GPU is a plus), and related power management design/DV techniques.

• Must be experienced with Incisive Low-Power or Synopsys VCS NLP

• Strong debug skills and experience with Verdi is needed

• Must be fluent in Verilog, SystemVerilog, and understanding of UVM.

Ways to stand out from the crowd:

• Prior knowledge of Low Power Architecture, Low Power CV, and deep learning

• Good understanding of power intent in UPF format is a plus

• A strong background in Low Power architectures or verification is a plus.

• Scripting abilities in Python or PERL is a plus and knowledge of C or C++ is a plus

• Experience writing or maintaining the script or Makefile that builds the simulation program is a plus

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking 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.

What you'll be doing:

• As a key member of the GPU Design team, you will implement, document and deliver high performance, area and power efficient RTL to achieve design targets and specifications.

• Analyze architectural trade-offs based on features, performance requirements and system limitations.

• Craft micro-architecture, implement in RTL, and deliver a fully verified, synthesis/timing clean design.

• Collaborate and coordinate with architects, other designers, pre- and post-silicon verification teams, synthesis, timing and back-end teams to accomplish your tasks.

• Work on a broad list of IPs such as GPU's work scheduler, time distribution system, interrupt controllers, and DMA engines.

• Architect features to help silicon debug and support post-silicon validation activities.

What we need to see:

• Bachelors Degree or equivalent experience in Electrical Engineering, Computer Engineering or Computer Science.

• Experience in micro-architecture and RTL development (Verilog).

• Good understanding of ASIC design flow including RTL design, verification, logic synthesis and timing analysis.

• Exposure to Digital systems and VLSI design, Computer Architecture, Computer Arithmetic is required.

• Strong interpersonal skills and an excellent teammate.

Ways to stand out from the crowd:

• Strong C/C++, Python or Perl skills.

• Good debugging and analytical skills.

• Experience with arbiters, scheduling, synchronization & bus protocols, interconnect networks, caches