Senior / Staff Research Scientist - Voice AI Foundations

Deepgram - San Francisco, CA

Full-time - Senior

San Francisco, CA

51-100 employees

About the position

Deepgram is a foundational AI company on a mission to transform human-machine interaction using natural language. We provide developers with access to the fastest and most powerful voice AI platform, which includes models for speech-to-text, text-to-speech, and spoken language understanding through a simple API call. Our solutions range from transcription to sentiment analysis and voice synthesis, making Deepgram the preferred partner for builders of voice AI applications. Despite the rise of text-based communication, voice remains the preferred medium for human interaction with machines. Delivering real-world voice AI solutions to our customers' most challenging problems is at the heart of our mission. At Deepgram, you will have the unique opportunity to innovate, experiment, and build, significantly shaping our products and AI capabilities. We value tenacious problem-solving and the ability to iterate, learn, and adapt. Domain-specific expertise in speech or language AI is not required, and you are encouraged to deepen your skills on-the-job, broadening your knowledge and expertise through constant iteration and invention. Our start-up environment offers a stunning growth trajectory due to a level of ownership and an on-ground connection with end-customers that larger research labs simply cannot provide. As a Senior/Staff Research Scientist at Deepgram, you will apply your skills to uncover breakthroughs that define the future of voice-enabled applications and experiences. Your work will involve harnessing vast audio and text datasets to train foundation models that go beyond transcribing speech and comprehending text. The models you build will unlock nuanced meanings in complex conversations, adapt robustly to diverse speech patterns, and generate empathic responses with human-like, contextualized speech. You will collaborate with product engineering to help deploy these models in the most scalable voice API on the planet. We look forward to you bringing your whole self to work, sharing learnings from your latest experiments, and collaborating with us to advance the state of AI and voice technology.

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.

Requirements

• 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.

Nice-to-haves

• 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.

Benefits

• Competitive salary and equity options
• Health, dental, and vision insurance
• Flexible work hours and remote work options
• Professional development opportunities
• Generous paid time off policy