Deepgram
ML Ops Infrastructure Engineer
USA | Remote
Sponsorship not specifiedDetected 107 days ago
PythonDockerKubernetesTerraformCI/CDPrometheusGrafanaDatadogDevOpsMachine LearningMLOpsA/B TestingResearchCollaborationProblem Solving
About the role
- 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.
- There is no organization in the world that understands voice better than Deepgram.
- 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.
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
- Collaborate with research engineers to define and enforce model quality gates before production promotion
- Optimize model serving infrastructure for latency, throughput, and cost efficiency
Requirements
- 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
- Experience with model serving frameworks such as NVIDIA Triton Inference Server, TensorRT, or ONNX Runtime
- Experience with Infrastructure as Code tools such as Terraform or Pulumi
- Hands-on experience with monitoring and observability stacks (Prometheus, Grafana, Datadog, or similar)
- Experience with feature stores, data versioning, or ML metadata management
Compensation
- Annual wellness stipend
Benefits
- Build observability dashboards that give the team real-time insight into model health across all environments
Company info
- 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.
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This listing is sourced directly from Deepgram's careers page and normalized into a canonical job model.