ML Infrastructure Engineer
Tasks
- Architect and deploy cloud and on prem ML training and evaluation infrastructure
- Build data management pipelines for ingestion storage and model training and evaluation
- Define requirements for deployable model design
- Deliver end to end ML infrastructure projects
- Develop processes to transition models from research to production
- Evaluate and implement methods software and hardware for model deployment
- Mentor engineers as needed
- Own ML infrastructure for autonomous vehicle model development and deployment
- Update model training code for improved data formats and storage techniques
Perks/Benefits
- N/A
Skills/Tech-stack
Apache Airflow | Apache Spark | Argo | Cloud platform | Data Engineering | Databricks | Distributed Systems | Google Cloud | Google Cloud Platform | Infrastructure as Code | Jetson | Kubernetes | MLOps | Machine Learning | Model Monitoring | Node.js | ONNX | Python | SageMaker | TensorRT | Terraform | Vertex AI | Workflow Orchestration | “as-code”
Education
N/A
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