ML Ops Infrastructure Engineer

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

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