ML Ops Infrastructure Engineer

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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

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

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

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