---
title: 'ML Ops Infrastructure Engineer at Deepgram'
canonical: 'https://feeny.ai/job/ml-ops-infrastructure-engineer-deepgram-united-states-1htdz6pnwj7e'
type: 'job'
last_seen: '2026-09-06'
---

# ML Ops Infrastructure Engineer at Deepgram

- **Company:** Deepgram
- **Location:** United States
- **Compensation:** $160k–$220k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-04-06
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/deepgram/7ced4c1f-4126-44fe-9cf1-da427a9e4e3e/application

**Skills:** Python, CI/CD, Docker, Kubernetes, MLOps, DevOps, Model Serving, Monitoring, Observability, Infrastructure as Code, NVIDIA Triton Inference Server, TensorRT, ONNX Runtime, Terraform, Pulumi, Prometheus, Grafana, Datadog, GPU-accelerated inference, Feature stores

> Design and build CI/CD pipelines, deployment systems, and testing infrastructure to bridge research models with production APIs. The role involves architecting model deployment pipelines, implementing monitoring and automated retraining, and ensuring reliable, scalable model delivery for millions of requests.

## Job description

## COMPANY OVERVIEW

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

Getting a model from a research notebook to a production API serving millions of requests is one of the hardest problems in AI. As an ML Ops Infrastructure Engineer at Deepgram, you will own the critical bridge between research and production -- building the pipelines, deployment systems, and testing infrastructure that take models from experimental to battle-tested at scale. Your work ensures that every model improvement our research team makes can be safely, quickly, and reliably delivered to the customers who depend on Deepgram's APIs for real-time voice AI.

## WHAT YOU'LL DO

- Design and build CI/CD pipelines specifically tailored for ML model development, validation, and deployment
- Architect and maintain model deployment pipelines that move models from research environments through staging to production with confidence
- Build A/B testing infrastructure that enables controlled rollouts of new models and measures real-world performance impact
- Implement comprehensive monitoring for model performance in production -- accuracy metrics, latency, drift detection, and regression alerts
- Develop automated retraining pipelines that trigger on data changes, performance degradation, or scheduled cadences
- Create and maintain build and test environments that mirror production, giving researchers high-fidelity feedback before deployment
- Establish model versioning, artifact management, and rollback capabilities to ensure safe and reproducible deployments
- Collaborate with research engineers to define and enforce model quality gates before production promotion
- Build observability dashboards that give the team real-time insight into model health across all environments
- Optimize model serving infrastructure for latency, throughput, and cost efficiency

## YOU'LL LOVE THIS ROLE IF YOU

- Are excited by the challenge of operationalizing cutting-edge AI models at production scale
- Believe that great infrastructure is what turns research breakthroughs into customer value
- Enjoy designing systems that are automated, reliable, and self-healing
- Want to work on problems where minutes of latency reduction or percentage points of accuracy matter enormously
- Like collaborating across research and engineering teams to make the whole organization faster
- Are motivated by building the deployment and testing systems that back a platform serving over 200,000 developers

## IT'S IMPORTANT TO US THAT YOU HAVE

- 4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems
- Strong proficiency in Python and experience building automation and tooling for ML workflows
- Deep experience with CI/CD systems and building pipelines for software and model delivery
- Hands-on experience with Docker and Kubernetes for containerized workload management
- Practical experience deploying and serving ML models in production environments
- Familiarity with model evaluation, validation, and quality assurance processes
- Understanding of monitoring and observability principles as applied to ML systems
- Strong problem-solving skills and a bias toward automation over manual processes

## IT WOULD BE GREAT IF YOU HAD

- Experience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime
- Background in speech, audio, or real-time media ML systems
- Experience with Infrastructure as Code tools such as Terraform or Pulumi
- Hands-on experience with monitoring and observability stacks (Prometheus, Grafana, Datadog, or similar)
- Familiarity with GPU-accelerated inference optimization and profiling
- Experience with feature stores, data versioning, or ML metadata management
- Knowledge of canary deployment strategies and progressive delivery for ML models

Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com http://deepgram.com email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com.

## About Deepgram

## Company Overview
- **One-liner**: Deepgram is a foundational AI company building real-time voice AI infrastructure for developers and enterprises to power speech-to-text, text-to-speech, and voice agent applications.
- **Entity Type**: Private (Series C, Unicorn status)
- **Headquarters**: San Francisco, California, USA (remote-first, distributed across 20+ states and 5+ countries)
- **Founded**: 2015
- **Founders**: Scott Stephenson (CEO), Adam Sypniewski, Noah Shutty

## Core Business
- **Primary industry**: Voice AI / Artificial Intelligence / Speech Recognition
- **Target customers**: Developers, product teams, enterprises, and platforms embedding voice AI (B2B, B2D)
- **Mission statement**: To transform the way humans interact with machines by unlocking the productivity of the world through voice technology

## Products & Services
- **Speech-to-Text (STT)**: Real-time and batch transcription with models like Nova-3 (world’s most accurate real-time STT) and Flux Multilingual (first conversational STT in 10 languages). Available via cloud or self-hosted. [deepgram.com](https://deepgram.com/)
- **Text-to-Speech (TTS)**: Aura-2, professional-grade enterprise TTS for natural voice output. [deepgram.com](https://deepgram.com/)
- **Voice Agent API**: A unified API that combines STT, TTS, and LLM orchestration into a single endpoint, reducing complexity, latency, and cost for building conversational AI agents. [deepgram.com](https://deepgram.com/)
- **Custom Models**: Ability to train custom speech models for unique use cases and domain-specific vocabulary. [deepgram.com](https://deepgram.com/)
- **Saga (Voice OS)**: An operating system for voice applications, announced as a platform-level offering. [linkedin.com](https://www.linkedin.com/company/deepgram)
- **Deepgram for Restaurants**: Vertical solution for drive-thru and restaurant voice ordering (95%+ containment), powered by the acquisition of OfOne. [linkedin.com](https://www.linkedin.com/company/deepgram)

## Market Standing
- **Valuation**: $1.3 billion unicorn status (as of Series C in 2024) [deepgram.com](https://deepgram.com/about)
- **Key Metric**: $130 million in Series C funding (total raised: ~$202M+ including $72M Series B) [deepgram.com](https://deepgram.com/about)
- **Notable Investors/Partners**: Y Combinator, NVIDIA (proud partner), with Series C led by prominent venture firms [ycombinator.com](https://www.ycombinator.com/companies/deepgram)
- **Growth Signals**: Processed 50,000+ years of audio and 1 trillion+ words; trusted by 200,000+ developers and 1,300+ organizations; acquired OfOne for restaurant voice AI; launched Flux Multilingual, Nova-3, Aura-2, and Saga in rapid succession [linkedin.com](https://www.linkedin.com/company/deepgram)

## Competitive Advantages
- **Foundational AI moat**: Deepgram is a research-driven company that builds its own end-to-end deep learning models (not wrappers), giving them control over accuracy, latency, and cost.
- **Unified API advantage**: A single Voice Agent API that replaces the need to stitch together separate STT, TTS, and LLM components—reducing complexity and latency.
- **Scale and experience**: The most experienced voice AI platform in the world, having processed the largest volume of voice data (50,000+ years of audio).
- **Enterprise-grade reliability**: Offers on-premise/self-hosted deployment options for compliance-heavy industries (healthcare, finance, legal).
- **NVIDIA partnership**: Access to scalable GPU infrastructure for training and inference, differentiating them from competitors.

## Strategic Focus
- **Voice AI Economy leadership**: Positioning Deepgram as the infrastructure layer for the emerging "trillion-dollar Voice AI economy."
- **Multilingual expansion**: Launching Flux Multilingual to capture global markets beyond English.
- **Vertical industry penetration**: Moving beyond horizontal API into vertical solutions (restaurants via OfOne acquisition, medical transcription).
- **Agentic AI**: Doubling down on Voice Agent API and autonomous voice agents as the next growth vector.
- **Platform expansion**: Building Saga (Voice OS) to become the operating system for all voice applications.

## Why Work Here
- **Remote-first culture**: Deepgram is a remote-first company with employees across 20+ states in the US and 5+ countries. They offer freedom and flexibility to work from anywhere. [deepgram.com](https://deepgram.com/careers)
- **Strong benefits package**: Medical, dental, vision insurance; mental health support; unlimited PTO; 12 paid US holidays; parental leave; 401(k) with company match; wellness stipend; learning/education stipend; quarterly productivity stipend; one-time office upgrade stipend. [deepgram.com](https://deepgram.com/careers)
- **Inclusive environment**: Active Employee Resource Groups (ERGs) and a stated commitment to "every voice, heard and understood." [deepgram.com](https://deepgram.com/careers)
- **Annual offsites**: While remote-first, the company convenes annually for in-person offsites to foster collaboration and connection. [deepgram.com](https://deepgram.com/careers)
- **Engineering culture**: Values include "be curious," "grow together," "do the right thing," "think smart, act fast," and "put the customer first." The culture is described as self-motivated, positive, passionate, and competitive. [deepgram.com](https://deepgram.com/careers)
- **Research-driven environment**: Deepgram was founded by physicists and maintains a strong research team (Research Scientist, Research Engineer roles), offering the chance to work on cutting-edge AI. [ycombinator.com](https://www.ycombinator.com/companies/deepgram)
- **Salary transparency**: Publicly lists salary ranges for roles (e.g., Research Scientist $150K-$250K, SDR $50K-$60K), indicating compensation transparency. [ycombinator.com](https://www.ycombinator.com/companies/deepgram)

## Sources
1. [deepgram.com - Careers Page](https://deepgram.com/careers)
2. [deepgram.com - Product Page](https://deepgram.com/)
3. [deepgram.com - About Page](https://deepgram.com/about)
4. [ycombinator.com - Deepgram Profile](https://www.ycombinator.com/companies/deepgram)
5. [linkedin.com - Deepgram Company Page](https://www.linkedin.com/company/deepgram)

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