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 RhythmAt 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.
Deepgram is seeking a Senior Technical Program Manager (Engineering) to drive execution across complex, cross-functional programs involving both software and hardware. In this role, you’ll work closely with our engineering and research teams to manage large-scale, technically demanding initiatives—from inception through delivery.
You’ll thrive here if you enjoy creating clarity out of complexity, organizing ambiguous requirements into coherent roadmaps, and getting into the technical weeds to keep programs moving forward. We’re looking for someone who has led large, multi-disciplinary initiatives and can partner with researchers, engineers, and business stakeholders to align teams and outcomes.
What You’ll Do
Own the end-to-end delivery of technical programs, many of which span hardware, software, and research efforts
Define workstreams, set milestones, and coordinate execution across multiple teams and functions
Serve as the connective tissue between engineering, research, product, and operations to ensure aligned priorities and timelines
Proactively identify and resolve bottlenecks, trade-offs, and interdependencies
Dive deep into technical topics to ensure risks are understood and addressed early
Build lightweight processes and tooling to help teams scale sustainably
You’ll Love This Role If You
Are passionate about building reliable, scalable, cutting-edge AI infrastructure
Enjoy optimizing complex distributed systems for performance and cost
Love solving challenging problems in compute, networking and storage at global scale
Are excited about working with cutting-edge GPU infrastructure, and frontier research in ML architecture
Want to work at the intersection of humans and AI/ML systems
It’s Important To Us That You Have
5+ years of program management experience leading large, cross-functional initiatives
Strong technical acumen—bonus if you’ve been an engineer or worked closely with applied-ML teams
Experience coordinating programs that touch both hardware and software domains
AI forward program management practices and processes
Proven ability to thrive in high-growth or startup environments
Comfortable navigating ambiguity and helping others do the same
Excellent communication and stakeholder management skills
It Would Be Great if You Had
Familiarity with AI/ML workloads and their infrastructure requirements
Knowledge of cost optimization for cloud and on-premise infrastructure
Experience with multi-region, multi-cloud infrastructure deployment
Background in performance optimization for distributed systems
Experience with software-defined infrastructure and networking
Certification and/or hands on experience with relevant cloud platforms (AWS, GCP, Azure)
Experience with software-defined infrastructure and networking