ML Ops Infrastructure Engineer

Deepgram; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Deepgram is seeking an ML Ops Infrastructure Engineer to bridge research and production, building pipelines, deployment systems, and testing infrastructure for scalable real-time APIs. You will ensure safe, fast, and reliable delivery of model improvements to customers using Deepgram's voice AI APIs.

You will design automated retraining and monitoring, establish model versioning, and collaborate with researchers to uphold quality gates across environments.

Qualifications

• 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.

Responsibilities

• 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

Skills

Python

CI/CD

Docker

Kubernetes

ML systems

Monitoring

Tools

NVIDIA Triton Inference Server

TensorRT

ONNX Runtime

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