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Tier-1 employer and Bangalore location increase competition despite senior, niche generative-audio ML specialization.
Highly specialized deep-learning, GPU optimization, and generative-audio expertise limit transferability across industries.
PhD plus 10+ years and deep specialized ML, GPU, and distributed training requirements enforce very strict filters.
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Lead research, design, and development of advanced deep learning models for audio and speech domains, including generative AI architectures.
Define technical vision and R&D roadmaps for audio, speech, and multimedia deep learning algorithms aligned with hardware advancements.
Train, optimize, and productize large-scale generative models on distributed GPU clusters, collaborating across research, hardware, and product teams.
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 in generative AI models (Diffusion, GANs, Transformers, VAEs, NeRFs, etc.) with strong Python programming and software architecture skills.
Proficient in PyTorch and experienced with multi-modal data pipelines (video decoding, audio DSP, spectrogram analysis).
Experienced in leading development of state-of-the-art generative audio/speech architectures and scaling foundation models on GPU clusters.
Strong cross-functional collaborator with track record in productizing AI models and mentoring senior engineers/scientists.
Has demonstrated impact via commercial AI products or top-tier AI conference publications, with familiarity in GPU optimization or distributed training frameworks.