Deep Learning Engineer - Machine Learning Models

Key Responsibilities :

• Model Porting & Deployment : Port and deploy complex deep learning models from various frameworks (e.g., PyTorch, TensorFlow) to proprietary or commercial ML accelerator hardware platforms (e.g., TPUs, NPUs, GPUs).
• Performance Optimization : Analyse and optimize the performance of ML models for target hardware, focusing on latency, throughput, and power consumption.
• Quantization : Lead the efforts in model quantization (e.g., INT8) to reduce model size and accelerate inference while preserving model accuracy.
• Profiling & Debugging : Utilize profiling tools to identify performance bottlenecks and debug issues in the ML inference pipeline on the accelerator.
• Collaboration : Work closely with the, hardware, and software teams to understand model requirements and hardware capabilities, providing feedback to improve both.
• Tooling & Automation : Develop and maintain tools and scripts to automate the model porting, quantization, and performance testing workflows.
• Research & Innovation : Stay current with the latest trends and research in ML hardware, model compression, and optimization Qualifications :
• Experience : 8 to 10 years of professional experience in software engineering, with a focus on model deployment and optimization.
• Technical Skills :
• Deep expertise in deep learning frameworks such as PyTorch and TensorFlow.
• Proven experience in optimizing models for inference onNPUs, TPUs, or other specialized accelerators.
• Extensive hands-on experience with model quantization (e.g., Post-Training Quantization, Quantization-Aware Training).
• Strong proficiency in C++ and Python, with experience writing high-performance, low-level code.
• Experience with GPU programming models like CUDA / cuDNN.
• Familiarity with ML inference engines and runtimes (e.g., TensorRT, OpenVINO, TensorFlow Lite).
• Strong understanding of computer architecture principles, including memory hierarchies, SIMD / vectorization, and cache optimization.
• Version Control : Proficient with Git and collaborative development workflows.
• Education : Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related Qualifications :
• Experience with hardware-aware model design and co-design.
• Knowledge of compiler technologies for deep learning.
• Contributions to open-source ML optimization projects.
• Experience with real-time or embedded systems.
• Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps best practices.
• Familiarity with CI / CD pipelines and automated testing for ML models.
• Domain knowledge in areas like computer vision, natural language processing, or speech recognition.

(ref : hirist.tech)