Tasks:
- * Align training and inference algorithms
- * Analyze failure cases and develop active-learning workflows
- * Apply pruning, distillation, and quantization
- * Build automated data annotation and dataset pipelines
- * Debug edge deployment accuracy and performance
- * Design model architectures and training workflows
- * Develop data processing and model performance tools
- * Develop distributed multi-model training
- * Implement mixed-precision training and gradient checkpointing
- * Optimize data loading and augmentation
- * Optimize GPU memory usage and training throughput
- * Optimize models for resource-constrained edge devices
Perks/Benefits:
Skills/Tech stack required:
[Active Learning] [C++] [CUDA] [Data Augmentation] [Distributed Training] [Edge Deployment] [GPU Optimization] [Gradient Checkpointing] [Knowledge Distillation] [Linux] [LoRA] [Mixed Precision] [Mixed-precision training] [Mixture of Experts] [Model Compression] [Model Evaluation] [Model Pruning] [Multimodal Training] [Nsight Systems] [Python] [PyTorch] [Quantization] [TensorFlow] [TensorRT] [TVM]
Educational requirements:
[Master's Degree]
Role(s):
[AI Deployment Engineer] [Deep Learning Engineer] [Deployment Engineer] [Engineer] [Learning Engineer] [Learning Systems Engineer] [Machine Learning Engineer] [Machine Learning Systems Engineer] [Model Optimization Engineer] [Optimization Engineer] [Systems Engineer]