Research Scientist Embodied Agentic Ai - 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:

• Working as a key member of our cloud solutions team, you will be the go-to technical expert on NVIDIA's products, helping our clients architect and optimize GPU solutions for AI services.

• Collaborating directly with engineering teams to secure design wins, address challenges, usher projects into production, and offer support through the project's lifecycle.

• Acting as a trusted advisor to our clients, while developing reference architectures and best practices for running Microsoft AI workloads on NVIDIA infrastructure.

What We Need To See:

• 4+ years of experience in cloud computing and/or large-scale AI systems.

• A BS in EE, CS, Math, or Physics, or equivalent experience.

• A proven understanding of cloud computing and large-scale computing systems.

• Proficiency in Python, C, or C++ and experience with AI frameworks like Pytorch or TensorFlow.

• Passion for machine learning and AI, and the drive to continually learn and apply new technologies.

• Excellent interpersonal skills, including the ability to explain complex technical topics to non-experts.

Ways To Stand Out From The Crowd:

• Recent projects or contributions (for example, on GitHub) related to large language models and transformer architectures.

• Knowledge of Azure cloud and AzureML services.

• Experience with CUDA programming and optimization.

• Familiarity with NVIDIA networking technologies such as Infiniband.

• Proficiency in Linux, Windows Subsystem for Linux, and Windows.

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.

• 3 years+ 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:

• Strong experience in building/evaluating deep learning models, coding agents and developer tooling.

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

• Demonstrated ability 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.

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:

• You will own and evolve the Cosmos open-source and internal research codebases, crafting core infrastructure that supports our foundation model research and deployment.

• Refactor and modularize large research-driven code into clean, testable, maintainable libraries for use across teams.

• Integrate and adapt off-the-shelf models into our pipelines as preprocessors, postprocessors, or evaluation components.

• Build model-serving endpoints (e.g., with Gradio or FastAPI) to enable researchers and internal users to experiment with models interactively.

• Design, implement, and maintain evaluation pipelines, providing high-quality tooling to the broader team to measure model quality and track improvements.

• Improve configuration hygiene and reproducibility using systems like Hydra, and ensure smooth overrides, templates, and environment switching.

• Lead efforts in packaging and release of Python modules using modern tools (uv, just, pydantic) for both OSS and internal consumption.

• Set the standard for code health, test coverage, and release readiness across the team. Write documentation and automation to scale good practices.

What we need to see:

• Expert-level proficiency in Python, with a strong foundation in modular design, abstraction boundaries, and collaborative codebase evolution.

• Fluency with PyTorch, including the ability to run, debug, and patch inference-time model behavior in research-level codebases. Comfort modifying pre/post-processors, model wrappers, and checkpoint logic.

• Proven experience in refactoring large codebases—cleaning up legacy implementations, eliminating anti-patterns, and paying down tech debt to improve long-term maintainability.

• Strong grasp of configuration systems, especially Hydra, with an emphasis on reproducibility, override logic, and environment scoping.

• Familiarity with Python packaging tools like uv, just, and pydantic, including experience managing environment consistency and shipping libraries as artifacts.

• Strong instincts around code health: API design, directory structure, writing unit and integration tests, exception hygiene, docstrings, and dependency isolation.

• Comfortable deploying models internally via Gradio or similar frameworks to enable interactive evaluation and feedback from researchers or downstream users.

• BS or MS (or equivalent experience) in Computer Science, Software Engineering, or a related technical field and 10+ years of industry experience.

Ways to stand out from the crowd:

• Proficiency in model configs, especially Hydra! Comfortable crafting hierarchical config systems with reusable templates, environment scoping, and overrides for evaluation, inference, or release.

• Prior work cleaning up sophisticated generative model codebases—adding tests, improving wrappers, and instrumenting code for observability and debugging.

• Demonstrated success raising engineering quality in a research setting: taking exploratory code and evolving it into a robust, production-friendly module.

• Track record of mentoring teammates on software engineering best practices and proactively identifying long-term structural risks in fast-moving teams.

• Passion for building ML tooling that is not only functional, but also elegant, intuitive, and maintainable by others.

What you'll be doing:

• Own and complete the building and development of reporting solutions across SFDC, Wrike, Power BI, and related platforms based on field and management needs.

• Lead needs assessments and requirements analyses to identify efficient tools and processes for business reporting.

• Build, analyze, and present data-driven insights and forecasts, offering actionable recommendations to collaborators.

• Develop and maintain NVIS dashboards that deliver clarity and performance visibility across key operational metrics.

• Collaborate with cross-functional teams—engineering, operations, planning, OEM partners, and logistics—to document and optimize lifecycle processes.

• Serve as the primary contact for Data Analytics process blocking issues for lifecycle accountability, ensuring efficient and transparent issue resolution.

• Monitor operational performance, document workflows, and drive continuous improvement initiatives across data and logistics functions.

What we need to see:

• Bachelor’s degree or equivalent experience.

• 5+ years of experience overall, with at least 4 years in data reporting, process mapping, supply chain, logistics, or related program management.

• Verified background in building dashboards and analytics through SFDC, Wrike, Power BI, or related tools.

• Strong understanding of information systems integration, reporting methodologies, and data visualization guidelines.

• Excellent analytical, problem-solving, and communication skills for cross-functional collaboration.

• Demonstrated ability to detail and refine processes, manage reporting tasks, and synthesize insights for executive audiences.