Company Overview
Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.
Company Operating Rhythm
At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.
Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.
Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.
Opportunity:
Deepgram is looking for a backend software engineer to lead the design and implementation of Deepgram’s internal data and ML training systems. You will design and implement secure, robust, and scalable services for data cataloging, querying, and access; efficient, distributed compute orchestration for ML training jobs; integrations with Research tooling, and more. Your skill at building highly reusable code that overcomes technical challenges will be paired with an intuition for what improves researcher productivity and model quality. You will be a critical voice in Deepgram’s Product, Data, and Engineering teams, driving high impact products from start to finish.
What You’ll Do
Improve Deepgram’s core data ingestion, cataloging, and transformation services including areas in networking, speech processing, audio transcoding, and latency and memory optimization
Engineer ML training code to be performant and highly extensible, rapidly enabling large-scale training of new foundational model architectures
Develop processes for measuring, building, and optimizing services to maximize system performance
Debug complex system issues that include networking, scheduling, database, and other persistence store interactions
Partner with DataOps and Research to design and implement new services, features, and/or products end to end
You’ll Love This Role If You
Thrive in a fast-paced, impact-driven environment where learning new skills on-the-fly is not only encouraged but a regular necessity
Enjoy balancing decisions about product and feature maturity to decide when to make minimally invasive changes versus when to incorporate detailed design work
It’s Important To Us That You Have
3+ years of experience in an industry role
Programming experience in Rust (or C, C++), with competence in Python
Excellent communication and organizational skills, both written and verbal.
A high level of experience and understanding of version control; preferably git.
Comprehensive experience with UNIX-style systems.
It Would Be Great if You Had
Experience with modern machine learning, such as experience with a framework like Torch or implementation knowledge of architectures like CNNs, RNNS, and transformers
Experience with audio processing