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, data isn’t just fuel for our models, it’s a product in its own right. Our vertically integrated voice AI platform depends on strategically created, curated, and labeled audio: from original data collection and augmentation, to structured workflows and evaluation sets. These data pipelines support a range of cutting-edge technologies, including audio intelligence models, conversational voice agents, STT, and TTS.
We’re looking for a hands-on, systems-minded Program Manager (Data Operations) to lead the design and execution of various voice data programs. This role is ideal for someone who thrives on building from scratch, someone who can take an abstract modeling goal or product need and turn it into a concrete data strategy and pipeline with tools, guidelines, and quality safeguards in place. This requires ownership to understand frontier research strategies, building custom style guides, prototyping new tools, and directly influencing how data shapes our products.
This is a role for builders, someone who can spot a gap, roll up their sleeves, and design the solution. You’ll be at the center of Deepgram’s model development cycle, working across Research, Engineering, and Product, and you’ll be elbow-deep in both the day-to-day execution and the systems thinking required to scale it.
Design, launch, and own end-to-end data workflows: from raw audio ingestion to production-ready datasets
Build and evolve labeling specs, style guides, and instructional documentation for global annotation teams
Identify opportunities for better tooling, automation, and workflow optimization, and lead their implementation
Translate product goals and model requirements into data creation strategies, deciding what to build, how to build it, and why it matters for product impact
Prototype and deploy data tools and infrastructure (e.g. Label Studio, custom Python scripts)
Collaborate with Research and Engineering to align data collection with model training architecture and downstream product impact
Track advancements in speech AI research and evolving market use cases to inform labeling approaches and data design priorities
Partner with QA and Evaluation leads to deliver high-quality, human-in-the-loop datasets and benchmarks
Manage and mentor data vendors, freelancers, and potentially internal ICs as the team grows
Track throughput, data quality, and vendor performance
Drive continuous improvement in speed, cost-efficiency, and quality across all data operations
Curate and refine datasets to align with specific product goals, linguistic coverage, or research hypotheses
Believe that data is a product, not just a resource, and you want to help define what great voice data looks like
Enjoy turning experimental ideas into robust, repeatable systems that can scale to production
Like to build systems that sit at the intersection of model performance, product impact, and operational excellence
Thrive in ambiguity and take initiative without waiting for perfect specs
Prefer action over perfection and iteration over indecision
Are energized by working with voice, speech, and the messy beauty of human language
4+ years of experience in technical data operations, ML programs, or hands-on AI data workflows
Demonstrated ability to design and build scalable processes, not just manage existing ones
Strong documentation, communication, and project leadership skills
Ability to work cross-functionally across technical and non-technical teams
Experience managing or working closely with vendors, freelancers, or distributed teams
Deep curiosity about how high-quality data shapes the performance of real-world ML products
Familiarity with Python and data visualization tools
Experience designing datasets for multilingual, low-resource, or domain-specific applications
Experience building or configuring annotation platforms like Label Studio
Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $85 million in total funding. If you're looking to work on cutting-edge technology and make a significant impact in the AI industry, we'd love to hear from you!
Deepgram is an equal opportunity employer. We want all voices and perspectives represented in our workforce. We are a curious bunch focused on collaboration and doing the right thing. We put our customers first, grow together and move quickly. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.
We are happy to provide accommodations for applicants who need them.
Originally posted on Himalayas
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