ML Infrastructure Engineer
Tasks
- Architect cloud and on prem ML training and evaluation infrastructure
- Build data management pipelines from ingestion to training and evaluation
- Create processes for research to production model transition balancing cost
- Deliver projects end-to-end
- Deploy and evaluate models on test and production vehicles
- Develop cloud data infrastructure for machine learning
- Implement model deployment monitoring and MLOps
- Optimize model training using improved storage formats and techniques
- Own ML infrastructure for autonomous vehicle model development and deployment
Perks/Benefits
- N/A
Skills/Tech-stack
Apache Airflow | Apache Spark | Argo Workflows | Cloud platform | Data Management | Data Pipelines | Distributed Systems | Distributed Training | Google Cloud | Google Cloud Platform | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Node.js | ONNX | Python | TensorFlow | TensorRT | Terraform | Workflow Orchestration | “as-code”
Education
N/A
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