Research Scientist (AI Model Optimization), IPV, ARTC

Job Description

• Lead research into state-of-the-art optimization techniques, including Quantization-Aware Training (QAT), Pruning, Knowledge Distillation, and Neural Architecture Search (NAS) to minimize latency.
• Design and implement scalable AI deployment architectures that can handle high-throughput data streams from multiple high-resolution cameras and process sensors simultaneously.
• Conduct hardware-software co-design to optimize models for specific deployment targets (e.g., NVIDIA Jetson, TensorRT, FPGAs, or specialized AI accelerators).
• Develop and manage asynchronous data pipelines that ensure zero-bottleneck performance from image acquisition to "final sentencing" decisions.
• Establish rigorous performance profiling benchmarks to track model latency and memory footprint across various manufacturing environments.
• Work with the System Integrator (SI) to ensure that optimized models are seamlessly integrated into the factory-level software stack.

• Ph.D. in Computer Engineering, Computer Science, Electrical Engineering, or a related field with a focus on High-Performance AI.
• Deep understanding of AI Inference Engines (e.g., TensorRT, ONNX Runtime, OpenVINO).
• Mastery of Model Compression techniques (Pruning, Quantization, Distillation).
• Expertise in C++ and Python for high-performance implementation.
• Hands-on experience with Parallel Computing (CUDA, OpenCL).
• Familiarity with Mixed-Precision Training and FP16/INT8 deployment.
• Proven ability to architect end-to-end AI systems that balance the trade-off between throughput, latency, and model precision.