We're now looking for a Senior Software Engineer, Cosmos Infrastructure and End to End Performance! NVIDIA Cosmos is an open omni-model platform of generative world foundation models (WFMs) designed to accelerate physical AI. By combining world generation, physical reasoning, and action generation into unified systems, Cosmos helps developers simulate physical environments and train robots, autonomous vehicles, and smart spaces. You can explore the project via the NVIDIA Cosmos GitHub or look up releases on Hugging Face.
Our team's mission is to build the foundational platform for Physical AI ecosystem enablement, empowering developers to create, train, evaluate, and deploy Physical AI systems through open frontier models and open SOTA training and data curation frameworks.
What you will be doing:
Building SoTA, World foundation models (https://arxiv.org/pdf/2606.02800) (like Cosmos3);
Engage with driving end to end performance analysis and drive HW-SW codesign for both the data center infrastructure and Edge deployments
Engage with customers to ensure the Cosmos models are easy to use and enabling the ecosystem;
Develop infrastructure to improve and automate the entire process of data ingestion, curation, pre-training, post-training, Export/quantization and deployment on the edge;
Design for robustness and fault tolerance.
What we need to see:
A Masters in Computer Engineering, Computer Science, Electrical Engineering or related STEM degree or equivalent experience. 5 years of relevant work experience
Expertise in working with large scale parallel and distributed accelerator-based system systems
Expertise optimizing performance and AI workloads on large scale systems. Experience with performance modeling and benchmarking at scale
Proficiency in Distributed PyTorch; Python, C/C++. A strong background in Computer Architecture, Networking, Storage systems, Accelerators
Understanding of DNNs and their use in emerging AI/ML applications and services
Expertise with at least one of public CSP infrastructure (GCP, AWS, Azure, OCI, …)
A deep understanding of World Foundation Models and their application to Physical AI
Experience developing infrastructure to automate multimodal data ingestion and curation. Experience driving tokenization and data set preparation is a plus.
Prior experience building transformer models (autoregressive and diffusion) or building the framework for and driving Post training - fine tuning and RL algorithms
Understanding of how to optimize for inference - export, quantization and containerization
Ways to stand out from the crowd:
Familiarity with popular AI frameworks (TensorFlow, JAX, Cosmos, Megatron-LM, Tensort-LLM, VLLM) among others.
Proficiency in CUDA
Very high intellectual curiosity; Confidence to dig in as needed; Not afraid of confronting complexity; Able to pick up new areas quickly with excellent interpersonal skills
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Master’s degree in Computer Engineering, Computer Science, Electrical Engineering, or a related STEM field, or equivalent experience
- Five years of relevant work experience
- Expertise with large-scale parallel and distributed accelerator-based systems
- Experience optimizing performance and AI workloads on large-scale systems
- Experience with performance modeling and benchmarking at scale
- Proficiency in Distributed PyTorch, Python, C, and C++
- Strong background in computer architecture, networking, storage systems, and accelerators
- Understanding of deep neural networks and AI/ML applications and services
- Expertise with at least one public cloud service provider, including GCP, AWS, Azure, or OCI
- Understanding of world foundation models and their applications to Physical AI
- Experience developing infrastructure for automated multimodal data ingestion and curation
- Experience with tokenization and dataset preparation
- Experience building autoregressive or diffusion transformer models
- Experience building post-training frameworks or driving fine-tuning and reinforcement learning algorithms
- Understanding of inference optimization, export, quantization, and containerization
- Familiarity with TensorFlow, JAX, Cosmos, Megatron-LM, TensorRT-LLM, or vLLM
- Proficiency in CUDA
- Strong intellectual curiosity, ability to handle complexity, adaptability, and interpersonal skills
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
