Deep Learning Engineer

3 weeks ago

Hyderabad, India Stealth Mode Startup - AI Product Based Company Full time

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)

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