ML Infrastructure Engineer
Tasks
- Build cloud data infrastructure system
- Define model requirements for successful deployment
- Deliver projects end-to-end
- Deploy models to test and production vehicles
- Deploy on prem ML training infrastructure
- Design ML evaluation infrastructure
- Develop machine learning training infrastructure
- Evaluate and implement deployment methods software and hardware
- Improve transition from research to production balancing cost
- Manage data pipelines from ingestion to storage
- Mentor engineers
- Optimize model training data formats and techniques
- Support model training and evaluation across vehicle compute cloud and on prem
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Amazon SageMaker | Apache Spark | Argo Workflows | C++ | Cloud platform | Google Cloud | Google Cloud Platform | Google Vertex | Google Vertex AI | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Model Deployment | Model Monitoring | Node.js | ONNX | Python | ROS | Robotics | TensorRT | Terraform | Vertex AI | “as-code”
Education
N/A
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