Responsibilities

• 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 int...