Deep Learning Engineer
2 weeks ago
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 techniques.
Required 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 field.
Preferred 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.
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