Technical Program Manager, Research

Deepgram

22h

About The Position

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. The Opportunity Voice AI stands at the cusp of a paradigm shift. Current approaches to voice interaction are fundamentally limited by the scarcity and diversity of audio data, combined with the prohibitive computational costs of processing high-dimensional audio at scale. These challenges have created a gap between the promise of universal voice interaction and today’s reality. While our research scientists pioneer new approaches in Latent Space Models (LSMs) to solve these fundamental challenges, we need exceptional technical program leadership to turn these breakthroughs into shipped products. The complexity of our mission (spanning audio compression, generative modeling, and multimodal systems) demands rigorous cross-functional execution, precise dependency management, and relentless focus on delivering the technical systems that turn research breakthroughs into real-world impact. As our research matures and the path from experiment to product grows shorter, we need a Technical Program Manager embedded in the work: someone who drives delivery, owns risk, and makes sure Research, Product, DataOps, and Engineering are always moving together. You will be embedded in Deepgram’s Research organization, working at the intersection of research output and technical delivery. This is not a role where “done” is always easy to define. You’ll be working with teams where timelines shift as experiments succeed or fail, where a model that looked ready last week needs another iteration this week, and where earning the trust of researchers matters as much as building the right process. Your job is to bring structure and delivery discipline to that environment without making it feel like a software release cycle. When you arrive, you’ll inherit a portfolio of active cross-functional programs spanning STT and TTS model development, data pipeline work, and evaluation infrastructure. In your first 90 days, success looks like: having a clear map of what’s in flight, who owns what, and where the risks are; having established a working rhythm with Research, Product, DataOps, and Engineering leads; and having driven at least one meaningful delivery milestone across teams. Concretely, over time you will:

Requirements

5+ years of experience as a Technical Program Manager or Engineering Program Manager, ideally with meaningful time spent supporting or embedded in an AI/ML Research organization
In-depth, working knowledge of how machine learning systems are built (i.e., data pipelines, model training, evaluation, and serving) that allows you to engage meaningfully with researchers and engineers on technical tradeoffs
Proven track record of delivering complex, multi-workstream technical programs on time across teams with competing priorities
Demonstrated ability to map cross-functional dependencies and technical risk across concurrent projects, with clear status communication to all stakeholders
Experience designing lightweight processes that improve execution velocity and accountability in research or engineering environments without adding bureaucratic overhead
Strong analytical skills for evaluating program health, spotting bottlenecks early, and making data-informed prioritization decisions
Excellent communication in both directions: crisp updates for senior leadership, clear direction for researchers
A pragmatic, roll-up-your-sleeves orientation: we need you to be equally effective at the strategic level and in the details

Nice To Haves

Bonus points for experience with speech or audio projects — audio data collection and labeling, audio model evaluation (including human eval), or shipping products where audio quality is the core user experience

Responsibilities

Own end-to-end delivery of new model versions and features for our flagship STT and TTS offerings, coordinating tightly across Research, Product, DataOps, and Engineering to hit milestones and ship on time
Define and champion lightweight but effective processes that bring visibility and accountability to researchers’ daily workflows without adding unnecessary overhead or stifling scientific creativity
Map dependencies and risks across all active cross-functional projects; maintain a clear picture of where things stand, where they’re at risk, and what needs to happen to keep execution on track
Run cross-functional programs end-to-end: scope definition, timeline management, stakeholder communication, risk mitigation, and launch readiness
Troubleshoot cross-team blockers quickly and build reliable partnerships across the technical organization so that handoffs between teams are smooth and accountability is clear
Own proactive communication across all levels of the organization: leadership should always have an accurate read on project health, and teams should always know how their work fits into the bigger picture
Respond quickly to shifting priorities, translating decisions made upstream into clear implications for timelines, team capacity, and cross-functional dependencies