About this role
Job Title: Software Engineer (2-3 years experience) Job Summary 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 collaborative 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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