ML Infrastructure Engineer
Tasks
- Architect cloud and on prem ML training evaluation infrastructure
- Define model deployment requirements
- Deliver projects end-to-end
- Deploy and evaluate ML models on test and production vehicles
- Improve research to production model transition
- Manage data pipelines from ingestion to evaluation
- Mentor or manage engineers
- Optimize model training code for storage formats
- Own ML infrastructure for autonomous vehicles
Perks/Benefits
- N/A
Skills/Tech-stack
Amazon SageMaker | Apache Airflow | Argo Workflows | C++ | Cloud platform | Databricks | Dataproc | Google Cloud | Google Cloud Platform | Google Cloud Vertex | Google Cloud Vertex AI | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Model Monitoring | NodeJs | ONNX | Python | ROS | Robotics | Spark | TensorRT | Terraform | Vertex AI | Workflow Orchestration | “as-code”
Education
N/A
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