Job Description
Exp: 2-3 years We're looking for a skilled and motivated Machine Learning Software to join our team. The ideal candidate will have a solid foundation in deep learning and a strong interest in optimizing and deploying ML models on specialized hardware. This role involves implementing model optimizations, with a particular focus on quantization, to improve the performance of machine learning inference on target platforms.
Key Responsibilities
Model Porting & Deployment: Port and deploy deep learning models from frameworks like PyTorch and TensorFlow to proprietary or commercial ML accelerator hardware platforms.
Performance Optimization: Analyze and improve the performance of ML models for target hardware, focusing on latency and throughput.
Quantization: Contribute to model quantization efforts (e.g., INT8) to reduce model size and accelerate inference while maintaining model accuracy.
Profiling & Debugging:
Use profiling tools to identify and fix performance bottlenecks in the ML inference pipeline on the accelerator.
Required Qualifications
Technical Skills: Proficiency in deep learning frameworks such as PyTorch and TensorFlow. Hands-on experience with deploying and optimizing models on GPUs or other specialized accelerators. Some experience with model quantization (Post-Training Quantization). Strong proficiency in C++ and Python. Experience with GPU programming models like CUDA/cuDNN is a plus. Familiarity with ML inference engines and runtimes (e.g., TensorRT, OpenVINO, TensorFlow Lite). Foundational understanding of computer architecture principles.
Version Control: Proficient with Git and cooperative development workflows.
Education
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
Preferred Qualifications
Knowledge of hardware-aware model design. Familiarity with compiler technologies for deep learning. Experience with real-time or embedded systems. Knowledge of cloud platforms (AWS, GCP, Azure). Experience with CI/CD pipelines for ML models.
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