Machine Learning Engineer
Tasks
- Build training and data pipelines end to end
- Create visualization triage and root cause tooling
- Deploy and optimize real time edge inference models
- Develop detection segmentation tracking and classification models
- Implement synthetic data generation and domain randomization
- Improve multi sensor fusion for EO IR perception
- Monitor drift and runtime health metrics
- Stand up distributed multi GPU training and evaluation
Perks/Benefits
Skills/Tech-stack
Active Learning | C++ | CI | CI Based Validation | CPU GPU | CPU/GPU profiling | CUDA | DVC | Data Augmentation | Data Generation | Data Mining | Dataset versioning | Distributed Training | Docker | Domain Randomization | Drift Detection | Edge inference | Experiment tracking | GPU Profiling | Image Segmentation | Jetson | Labeling | Linux | MLOps | Model Registry | Multi Sensor | Multi-sensor Fusion | ONNX Runtime | Object Detection | Object Tracking | Parquet | Pruning | PyTorch | Python | Quantization | ROS 2 | SQL | Sensor fusion | Synthetic Data Generation | Synthetic data | TensorRT
Education
Regions
Countries
States
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