ML Infrastructure Engineer
Tasks
- Architect and deploy cloud and on prem ML training and evaluation infrastructure
- Define model requirements to ensure deployment success
- Deliver projects end-to-end
- Evaluate and implement methods for ML model deployment on test and production vehicles
- Improve transition from research to production while balancing cost
- Manage data ingestion and storage for vehicle compute cloud and on prem
- Optimize model training code using data formats
- Own ML infrastructure for autonomous vehicle model development and deployment
Perks/Benefits
Skills/Tech-stack
Amazon SageMaker | Apache Airflow | Apache Spark | Cloud platform | Databricks | Google Cloud | Google Cloud Platform | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Model Monitoring | Node.js | ONNX | Python | TensorRT | Terraform | Vertex AI | Workflow Orchestration | “as-code”
Education
N/A
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