Senior Deep Learning Research Engineer – Multimedia GPU team

NVIDIA is hiring a Senior Deep Learning Research Engineer – Multimedia GPU team in Bengaluru, India. Level rates it ; you can apply on NVIDIA.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
The posting mentions PyTorch, GPUs and Transformers among its tools and skills.
Level classes it as a senior role.
NVIDIA has 2,700+ other open roles on Level.

AI in this role

Lead research and development of state-of-the-art deep learning models and generative AI systems for audio and speech domains at NVIDIA.

pytorchgpustransformersdiffusion-models

deep-learninggenerative-aiaudio-processingdistributed-training

NVIDIA is a global leader in AI, high-performance computing, and visualization, with GPU technology powering everything from modern computers to robots, autonomous systems, and the metaverse. As the pioneer of AI computing, NVIDIA is shaping the future of next-generation multimedia, generative AI, and spatial computing.

We are looking for a visionary Senior Deep Learning Research Engineer to join our team. In this role, you will bridge the gap between Deep learning research and production, pioneering the next generation of audio foundation models. If you are passionate about solving ultra-complex, multi-modal AI problems that will redefine industry standards for entertainment, communication, and digital twins, come join us.

What you’ll be doing:

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.

What we need to see:

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).

Ways to stand out from the crowd:

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.

NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We do not discriminate based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.

How we rate this

Senior Deep Learning Research Engineer – Multimedia GPU team at NVIDIA rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Deep Learning Research Engineer – Multimedia GPU team is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in India, Bengaluru.
The job was posted on June 27, 2026.
Seniority: senior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Deep learningGenerative AIAudio ProcessingDistributed TrainingPyTorchGPUsTransformersDiffusion Models

Questions you could be asked

Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where generative ai was part of your work. What did you do?
Tell me about a project where audio processing was part of your work. What did you do?
Tell me about a project where distributed training was part of your work. What did you do?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Deep learning, Generative AI, Audio Processing, Distributed Training, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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