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Tier-1 brand and metro location increase competition, but senior specialized ML role limits broad applicant pool.
Deep research, PhD requirement, and GPU-focused generative-AI expertise limit cross-industry transferability.
PhD, 10+ years, and deep ML/GPU expertise make hiring filters highly stringent.
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Lead research, design, and development of state-of-the-art deep learning models for audio and speech domains.
Define technical vision and R&D strategy for NVIDIA's future audio, speech, and multimedia deep learning algorithms aligned with hardware.
Train, optimize, and productize large-scale generative AI models using distributed training across massive GPU clusters and deploy inference models on NVIDIA platforms.
PhD in Computer Science, Artificial Intelligence, Applied Mathematics, or related quantitative field.
10+ years of industry or post-doc experience developing deep learning models for audio, image, and video processing.
Expert-level proficiency with PyTorch and multi-modal data processing pipelines (video decoding, audio DSP, spectrogram analysis).
Elite Python programming skills and strong software architecture experience for production-grade scalable systems.
Demonstrated mastery of generative AI technologies including Diffusion models, GANs, Transformers, VAEs, and Neural Radiance Fields.
Proven ability to lead cross-functional collaborations and productize AI research into commercial products or peer-reviewed high-impact publications.
Experience with large-scale distributed training frameworks and low-level GPU optimization tools (CUDA, cuDNN, Triton, TensorRT) is a strong advantage.