Deepgram · 23 hours ago

Research Scientist - Voice AI Foundations

San Francisco, CA

Full-time

Remote

Senior Level

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Artificial Intelligence (AI)Data Collection and Labeling

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Responsibilities

Design and carry out experimental programs to build new speech and language AI foundation models across modalities and tasks, that solve critical problems for our customers.

Drive large-scale training jobs successfully on massive distributed computing infrastructure.

Optimize model architectures to make them as fast and memory-efficient as possible; deploy new models into production for use at massive scale.

Document and present results and complex technical concepts clearly for internal and external audiences

Stay up to date with the latest advances in deep learning with a particular eye towards their implications and applications within our products.

Qualification

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Deep LearningPythonPytorchModel OptimizationDistributed ComputingData CurationModel BuildingArchitecture DesignTransformersParallelism ParadigmsExperimental Program Design

Required

PhD in Physics, Electrical Engineering, Computer Science or another related field

Prior experience in designing and conducting experimental programs aimed at understanding complex phenomena, with the ability to rapidly iterate and change course as needed.

Proven experience building models from a blank page and owning the entire deep learning stack including data curation, characterization and cleaning, architecture design and model building, distributed large-scale training, and model optimization for inference.

Strong communication skills and the ability to translate complex concepts in simple terms, depending on the target audience

Strong software engineering skills with particular emphasis on developing clean, modular code in Python and working with Pytorch.

Preferred

Prior industry experience in building deep learning models to solve complex problems, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanisms.

Deep understanding and experience working with state-of-the-art network architectures including transformers.

Understanding of different parallelism paradigms for efficient distributed training.