AI Systems Engineer IRC216136
2 weeks ago
Description:
Continuation of the project related to self-driving cars. Requirements: Core Technical Skills- Machine Learning and Deep Learning: Solid understanding of ML and deep learning principles, algorithms, and architectures. Familiarity with a range of models, including but not limited to CNNs, RNNs, and transformers, across various applications like image processing, natural language processing, and predictive analytics.
- Model Conversion and Optimization: Expertise in converting ML models to ONNX format for interoperability and further optimizing them using NVIDIA TensorRT for high-performance inference on NVIDIA Jetson devices.
NVIDIA Jetson Platforms: Hands-on experience with NVIDIA Jetson Nano, TX2, Xavier NX, or AGX Xavier, including setup, configuration, and performance tuning. Knowledge of Jetson software stack, including JetPack SDK. - Dependency Management: Proficiency in solving dependencies and compatibility issues during model conversion and deployment, including library versions, CUDA, and cuDNN dependencies.
- Programming Languages: Strong programming skills in C++, with experience in Python being a plus. Proficiency in using PyTorch, TensorFlow, or other deep learning frameworks for model development and training.
- Environment and Tools: Familiarity with Docker and virtual environments for managing project dependencies. Experience with version control systems, preferably Git, for code collaboration and versioning.
- Edge AI Applications: Understanding of Edge AI concepts and experience in deploying AI solutions on edge devices, optimizing for low power consumption and latency.
- Data Preprocessing: Skills in data augmentation, preprocessing, and transformation techniques to prepare datasets for efficient training and inference on edge devices.
- Benchmarking: Experience in conducting benchmarks to measure model performance (e.g., inference speed, accuracy, power consumption) on NVIDIA Jetson platforms.
- Troubleshooting: Ability to troubleshoot and debug hardware and software issues, including performance bottlenecks and memory constraints on edge devices.
- Solution Porting:
Collaborating with ML engineers to understand the intricacies of the models and algorithms being ported.
- Performance Optimization:
Implementation optimizations such as model quantization, pruning, and TensorRT conversion to improve inference speed and reduce model size.
- Integration and Testing:
Conduct thorough testing and validation to identify and resolve any performance bottlenecks or compatibility issues.
- Collaboration and Communication:
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