Senior Deep Learning Research Engineer – Multimedia GPU team

Pioneer Generative AI - Audio Architecture: Lead the research, design, and development of state-of-the-art deep learning models solving complex problems in audio and speech domains.

Drive Future Technology Roadmaps: Define the technical vision and R&D strategy for NVIDIA’s future Audio, Speech and multimedia DL algorithms, ensuring alignment with hardware advancements.

Scale Massive Foundation Models: Train and optimize large-scale generative models (Diffusion, Transformers, Autoregressive models) using distributed training across massive GPU clusters.

Audio-Speech-Visual Fusion: Develop advanced algorithms for speech transformation, spatial audio, audio enhancements, and real-time video/audio enhancements using Deep Learning Algos.

Cross-Functional Leadership: Collaborate closely with NVIDIA Research, hardware architecture teams, and product groups (such as NeMo, AI4Media, and Broadcast) to productize breakthrough technologies.

Productize and Deploy models on Edge: Develop and productize inference models on NVIDIA GPUs and Nvidia RTX Spark platforms as SDK/Microservices after optimization.

Technical Mentorship: Mentor senior engineers and scientists, foster a culture of technical excellence, and maintain high standards via code and design reviews.

PhD in Computer Science, Artificial Intelligence, Applied Mathematics, or a related quantitative field.

10+ years of industry or post-doc experience directly developing advanced deep learning models for audio, image, and video processing.

Deep Mastery of Generative AI: Proven track record of working with Diffusion models, GANs, Transformers, VAEs, Neural Radiance Fields (NeRFs) / 3D Gaussian Splatting, GRUs etc.

Strong Programming & Software Architecture: Elite Python coding skills with a solid foundation in production-grade software design, scalability, and data structures.

Framework Proficiency: Expert-level hands-on experience with PyTorch and deep familiarity with multi-modal data processing pipelines (video decoding, audio DSP, spectrogram analysis).

Proven Impact: A strong portfolio of shipped high-impact commercial AI products or a stellar publication record at top-tier AI conferences (CVPR, ICCV, SIGGRAPH, NeurIPS, ICASSP, Interspeech).

Deep understanding of Generative Audio/Speech Architecture and Algorithms

Experience with large-scale distributed training frameworks (e.g., Megatron-LM, DeepSpeed, PyTorch FSDP) on cluster architectures.

High proficiency in C++ and low-level GPU optimization tools like CUDA, cuDNN, Triton, or TensorRT .

Experience in building World Models or physics-informed neural networks for video synthesis would be an add-on.

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