דרושים Phd Research Intern Large Language Models - ב-אנבידיה ב-ארהב

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.

What you'll be doing:

• Designing and implementing cutting-edge techniques in the field of vehicle autonomy.

• Publishing your original research.

• Collaborating with other research team members, a diverse set of internal product teams, and external researchers.

• Transferring technology you've developed to relevant product groups.

What we need to see:

• Completing or recently completed a PhD in Robotics, Computer Science, Computer Engineering or related field (or equivalent experience).

• Relevant research experience in the field of vehicle / robot autonomy.

• Strong knowledge of theory and practice of vehicle / robot autonomy, or a related area with a strong interest in connecting your work to autonomous vehicles.

• A track record of research excellence with your work published in top conferences and journals such as RSS, ICRA, IJRR, NeurIPS, ICML, CVPR, TAC, etc, and other research artifacts such as software projects.

• Exceptional programming skills in Python; C++ and parallel programming (e.g., CUDA) are a plus.

• Knowledge of common machine learning frameworks such as PyTorch and Tensorflow.

• Strong communication and interpersonal skills are required along with the ability to work in a dynamic, research-focused team.

What you’ll be doing:

• Develop detailed, domain-specific benchmarks for evaluating world foundation models, especially generation and understanding world models that reason about video, simulation, and physical environments.

• Use sophisticated prompt engineering techniques to elicit structured, interpretable responses from a variety of foundation models.

• Build, refine, and maintain question banks, multiple-choice formats, and test suites to support both automated and human evaluation workflows.

• Employ multiple VLMs in parallel to explore ensemble evaluation methods such as majority voting, ranking agreement, and answer consensus.

• Make evaluation as automated and scalable as possible by encoding prompts and expected outputs into structured formats for downstream consumption.

• Interface directly with Cosmos researchers to translate their evaluation needs into scalable test cases.

• Collaborate with human annotators, providing clearly structured tasks, feedback loops, and quality control mechanisms to ensure dataset reliability.

• Meet regularly with domain experts in robotics, autonomous vehicles, and simulation to understand their internal benchmarks, derive transferable metrics, and co-develop standardized evaluation formats.
  

What we need to see:

• Demonstrated experience with prompt engineering, including crafting, refining, and optimizing prompts.

• Strong attention to detail in designing natural language questions and formatting structured evaluations.

• Proven ability to reason about model capabilities, failure modes, and blind spots in real-world generative model deployments.

• Experience crafting or contributing to benchmarks or evaluation datasets, especially for multimodal or agentic systems.

• Familiarity with evaluating models via prompting, capturing structured outputs, and comparing across model families.

• Excellent communication and collaboration skills—you will regularly meet with researchers, annotators, and downstream users to iterate on benchmark design.

• A working understanding of how VLMs and foundation models function at inference time, including token-level outputs, autoregressive decoding, and model context windows.

• 10+ years of experience in Machine Learning, NLP, Human-Computer Interaction, or related fields.

• BS, MS, or equivalent background. Prior experience in AI evaluation, annotation workflows, or research is highly valued.

Ways to stand out from the crowd:

• Hands-on experience with multiple LLMs or VLMs (e.g., GPT, Claude, Gemini, Flamingo, Kosmos, IDEFICS, etc.) to compare outputs and engineer task-specific prompts.

• Prior work designing benchmarks for robotics, simulation, AV, or agentic tasks, especially in multimodal or video-based settings.

• Experience working with human annotation teams, building clear instructions and QA processes for large-scale labeling campaigns.

• Familiarity with using VLMs as evaluators, leveraging models for response scoring, ranking, or consensus aggregation.

• Deep curiosity about model behavior and a drive to test, interrogate, and stretch the limits of generative systems.

What you'll be doing:

• Path-find technical innovations in Quantum Error Correction and Fault Tolerance, working with multi-functional teams in Product, Engineering, and Applied Research

• Develop novel approaches to quantum error correction codes and their logical operations, including methods for implementation and logical operation synthesis

• Research and co-design improved methods to achieve fault tolerance, such as techniques for logical operations, concatenation, synthesis, distillation, cultivation, or others

• Collaborate with internal teams and external partners on developing technology components to enable a fault-tolerant software stack integrated with quantum hardware

• Adopt a culture of collaboration, rapid innovation, technical depth, and creative problem solving

What we need to see:

• Degree in Physics, Computer Science, Chemistry, Applied Mathematics, or related engineering field or equivalent experience (Ph.D. preferred)

• Extensive background in Quantum Information Science with 8+ overall years of experience in the Quantum Computing industry

• A demonstrated ability to deliver high impact value in quantum error correction and fault tolerance

Ways to stand out from the crowd:

• Hands-on experience in scientific computing, high-performance computing, applied machine learning, or deep learning

• Experience with co-design of quantum error correction with quantum hardware or quantum applications

• Experience with CUDA and NVIDIA GPUs

• Passion to drive technology innovations into NVIDIA software and hardware products to support Quantum Computing