Deepgram
Backend Software Engineer - Active Learning
About this role
Deepgram seeks a backend engineer to design and build scalable data, ML training, and orchestration systems that power the company's voice AI platform. You'll work on data cataloging, distributed compute for model training, and research integrations while collaborating closely with product and research teams in a fast-paced, AI-driven environment.
What you'll do
- Build and improve data ingestion, cataloging, and transformation services for networking, speech processing, and audio handling
- Design performant and extensible ML training infrastructure for foundational model development
- Develop monitoring and optimization processes to measure and enhance system performance
- Diagnose complex issues across networking, scheduling, databases, and storage systems
- Partner with DataOps and Research teams to deliver end-to-end products and features
What they're looking for
- Rust, C, or C++ programming
- Python competence
- Distributed systems and ML training orchestration
- UNIX/Linux systems expertise
- Git and version control
- Audio processing (preferred)
- Machine learning frameworks like PyTorch (preferred)
- System debugging and performance optimization
Benefits
- Medical, dental, and vision coverage
- Annual wellness stipend
- Mental health support
- Life, STD, and LTD insurance
- Work/life blend flexibility
- Remote work (USA)
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Deepgram
Deepgram builds voice AI and speech processing technology, offering platforms that power real-time speech recognition and audio intelligence across cloud, edge, and embedded environments. The company is hiring applied ML engineers, backend engineers, embedded AI engineers, and customer-facing roles (customer success and pre-sales engineers) to scale its AI infrastructure and drive enterprise adoption.
- Website
- deepgram.com
Likely interview questions
- Walk us through your experience designing and implementing data cataloging or ML training systems. What were the key technical challenges and how did you solve them?
- Tell us about a time you optimized a backend service for performance. How did you identify bottlenecks and measure improvements?