Research Scientist (AI Model Optimization), IPV, ARTC

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A*STAR - Agency for Science, Technology and Research in Singapore is seeking an expert to lead research into optimization techniques for AI deployment. The ideal candidate will have a Ph.D. and extensive experience with AI inference engines and model compression techniques. Responsibilities include designing scalable architectures for high-throughput data streams and conducting co-design to optimize models for specific hardware. Join a cutting-edge team to advance high-performance AI solutions.

Qualifications

• Ph.D. in a related field with a focus on High-Performance AI.
• Deep understanding of AI Inference Engines like TensorRT and ONNX Runtime.
• Mastery of Model Compression techniques including Pruning and Quantization.

Responsibilities

• Lead research into optimization techniques to minimize latency.
• Design scalable AI deployment architectures for high-resolution data streams.
• Conduct hardware-software co-design for optimized model deployment.

Skills

C++

Python

Model Compression

AI Inference Engines

Parallel Computing

High-Performance AI

Education

Ph.D. in Computer Engineering, Computer Science, or Electrical Engineering

Job description

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 into the factory-level software stack.

Requirements

• 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.