Senior MLOps Engineer
Tasks
- Automate dataset selection
- Build QA workflows for labels
- Build data pipelines
- Build data quality dashboards
- Coordinate sensor calibration and data schemas
- Create continuous evaluation pipelines
- Define annotation standards
- Detect outliers
- Implement label consistency checks
- Improve training throughput
- Integrate ML pipelines into platform infrastructure
- Manage dataset versioning
- Manage labeling operations
- Manage regression tests
- Monitor model drift
- Optimize data loaders
- Run active learning and hard sample mining
- Support multi sensor fusion workflows
- Track metadata for reproducibility
- Track on vehicle performance signals
Perks/Benefits
- N/A
Skills/Tech-stack
2D Object Detection | 3D Object Detection | 3D Perception | AWS EC2 | Active Learning | Airflow | Amazon S3 | Annotation QA | Autoscaling | CUDA | Data Pipelines | Data Quality | Data Versioning | Data quality monitoring | Dataset Version Control | Depth Estimation | Distributed Systems | Drift Detection | Hard Sample Mining | IAM | Instance Segmentation | Labeling Workflow | Labeling Workflow Management | Lidar | MCAP | ML model monitoring | MLflow | Metaflow | Model Drift | Model Drift Detection | Model Monitoring | Multi Camera | Object Detection | Outlier Detection | PyTorch | PyTorch Lightning | Python | Quality monitoring | ROS2 | Ray | Real Time | Real-time Systems | Regression testing | Sample Mining | Semantic Segmentation | Sensor fusion | Spot instances | Telemetry | Time Systems | Version control | Weights and Biases | Workflow Management
Education
N/A
Roles
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