ML Ops Infrastructure Engineer

Company Overview

Deepgram is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.

The Deepgram Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models.

Operating Principles:

Deepgram is a research company that productizes as fast as possible. Four Operating Principles shape how we think, decide, and work together:

• Compress before you communicate. You think it through first, then lead with the bottom line.

• Resist entropy. You question what already exists before adding more process or tools.

• Solve the category. You fix recurring problems at the source, not one case at a time.

• Work at the frontier. You go after problems no one has solved yet.

We look for people who already work this way, and these principles shape how we grow and evaluate our team.

AI-First by Default

We build AI that millions of people talk to, and we run the company the same way. AI is how we centralize work here, and is core to how we operate.

That means more than using the tools. We expect you to:

• Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster.

• Own the output. AI can speed up the work. It doesn’t make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you.

• Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet.

• Keep pace. Models and tools change every month, and so will your work. You adopt what’s new quickly and drop what no longer earns its place.

We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment.

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 email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com.