Nvidia Senior ML Software Engineer 

United States, California 

902156209

What you'll be doing:

• Design, post-train, and optimize foundation models (e.g., LLMs, diffusion video models, VLMs, VLAs) for real world applications.

• Contribute to highly-collaborative development on large-scale training infrastructure, high-efficiency inference pipelines, and scalable data pipelines.

• Work with teams in research, software, and product to bring world models from idea to deployment.

• Collaborate on open-source and internal projects, author technical papers or patents, and mentor junior engineers.

• Prototype and iterate rapidly on experiments across cutting-edge AI domains, including agentic systems, reinforcement learning, reasoning, and video generation.

• Design and implement model distillation algorithms for size reduction and diffusion step optimization. Profile and benchmark training and inference pipelines to achieve production-ready performance requirements.

What we need to see:

• We are looking for stellar experience building and deploying generative AI systems (minimum 8 years industry or 5+ years research/postdoc).

• Proficiency in PyTorch, JAX, or other deep learning frameworks is a must!

• We are working on a full range of foundation models. You should have expertise in one or more of: LLMs, coding agents, diffusion models, autoregressive models, VAE/GAN architectures, retrieval-augmented generation, neural rendering, or multi-agent systems.

• Our models are predominantly built on the transformer architectures. You should be intimately familiar with all variants of the attention mechanisms.

• Hands on experience with large scale training (e.g., ZeRO, DDP, FSDP, TP, CP) and data processing (e.g. Ray, Spark).

• All we do is in Python and we open source our product, therefore production-quality software engineering skills is highly relevant.

• MS or PhD or equivalent experience in Computer Science, Machine Learning, Applied Math, Physics, or a related field.

Ways to stand out from the crowd:

• Familiarity with high-performance computing and GPU acceleration.

• Contributions to influential open-source libraries or influential conference publications (NeurIPS, ICML, CVPR, ICLR).

• Experience working with multimodal data (e.g., vision-language, VLA, audio).

• Prior work with NVIDIA GPU-based compute clusters or simulation environments.