ML Infrastructure Engineer
Tasks
- Architect cloud and on prem ML training and evaluation
- Build data management pipelines ingestion storage training evaluation
- Define systems and processes for model deployment readiness
- Deliver end-to-end projects
- Deploy models to test and production vehicles
- Improve transition from research to production
- Optimize model training code for data storage formats
- Own ML infrastructure for autonomous vehicles
Perks/Benefits
Skills/Tech-stack
Airflow | Amazon SageMaker | Apache Spark | Argo Workflows | Data Engineering | Databricks | Distributed Systems | Google Vertex | Google Vertex AI | Infrastructure as Code | Kubernetes | MLOps | Machine Learning | Model Monitoring | NVIDIA Jetson | Node.js | ONNX | Python | TensorRT | Terraform | Vertex AI | Workflow Orchestration | “as-code”
Education
N/A
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