Backend Software Engineer - Learning Team

Location

USA | Remote

Full time

Remote

Engineering

• Estimated Base Salary $150K – $220K
  • Offers Equity
  • Offers Bonus
  • 10% Annual Bonus

This range is determined by work location and additional factors, including job-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description. Please note that the compensation details listed on US role postings reflect the base salary only and do not include bonus, equity or benefits.

Deepgram is the leading voice AI platform for developers building speech-to-text (STT), text-to-speech (TTS) and full speech-to-speech (STS) offerings. 200,000+ developers build with Deepgram’s voice-native foundational models – accessed through APIs or as self-managed software – due to our unmatched accuracy, latency and pricing. Customers include software companies building voice products, co-sell partners working with large enterprises, and enterprises solving internal voice AI use cases.

The company ended 2024 cash-flow positive with 400+ enterprise customers, 3.3x annual usage growth across the past 4 years, over 50,000 years of audio processed and over 1 trillion words transcribed. There is no organization in the world that understands voice better than Deepgram.

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.

• 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 Data Ops and Research to design and implement new services, features, and/or products end to end

• 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

• 3+ years of experience in an industry role

• Programming experience in Rust (or…