Principal ML & AI Engineer (Spatial AI & Perception)
Tasks
- Build BEV scene representation models
- Build automated regression test pipelines
- Collaborate with research and simulation teams
- Compile models with QNN
- Compile models with TensorRT
- Design experiments with ablation studies
- Develop perception models
- Evaluate deployment readiness
- Export models to ONNX
- Integrate geospatial priors
- Maintain code quality and documentation
- Measure accuracy latency memory tradeoffs
- Mentor junior engineers
- Operationalize research architectures into training pipelines
- Optimize models for edge deployment
- Profile models on embedded hardware
- Recover accuracy after optimization
- Run distributed training pipelines
- Run quantization workflows
- Train infrastructure perception models
- Validate synthetic training data
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | Ablation Studies | Artificial Intelligence | B testing | BEVDet | BEVFormer | BEVFusion | Computer Vision | DDP | Data Augmentation | Deep learning | Distributed Training | Experiment tracking | FSDP | Gradient Checkpointing | Graph optimization | Instance Segmentation | Latency profiling | MLflow | Machine Learning | Mixed Precision | Model Compression | ONNX | Object Detection | Occupancy networks | PTQ | PyTorch | QAT | QNN | Quantization | Semantic Segmentation | Synthetic data | TensorRT | Transformer | Weights and Biases
Education
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