Physical AI (Edge Intelligence) Research Scientist

Key Responsibilities

• Conduct research on Physical AI, Embodied AI, Multimodal AI, and Foundation Models for real-world applications.

• Develop and fine-tune Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, multimodal models, and action-generation policies for physical systems.

• Design and implement AI algorithms for perception, reasoning, decision-making, and action generation using multimodal sensory inputs.

• Optimise AI models for efficient edge inference, including model compression, quantisation, pruning, knowledge distillation, and computational graph optimisation.

• Deploy and benchmark AI models on edge computing platforms and physical testbeds, integrating camera feeds and other sensor inputs.

• Use simulation environments for synthetic data generation, model pre-training, domain adaptation, and sim-to-real evaluation.

• Work with hardware acceleration engineers to optimise AI workloads for target edge platforms and achieve low-latency, real-time inference.

• Develop AI inference pipelines using frameworks and tools such as PyTorch, ONNX, TensorRT, and related deployment technologies.

• Conduct experiments, benchmark model performance, analyse results, and translate research ideas into working prototypes.

• Publish research findings in leading AI/ML, computer vision, robotics, or related conferences and journals.

• Identify opportunities for technical patents and intellectual property arising from research and engineering work.

Qualifications

• Ph.D. in Computer Science, Artificial Intelligence, Computer Vision, Robotics, Electrical Engineering, or a related quantitative discipline. Exceptional candidates with a research-oriented Master's degree and strong publications may also be considered.

• Strong research background in one or more of the following:

• Physical AI / Embodied AI

• Multimodal AI

• Vision-Language Models (VLMs)

• Vision-Language-Action (VLA) Models

• Foundation Models

• Computer Vision

• Robotics / Robot Learning

• Edge AI

• Strong understanding of Deep Learning, Transformers, multimodal models, or generative AI.

• Strong publication record in top-tier AI/ML, computer vision, robotics, or related conferences and journals.

• Strong proficiency in Python and PyTorch.

• Familiarity with AI model deployment and optimisation frameworks such as ONNX, TensorRT, or similar inference frameworks.

• Experience with model compression, quantisation, pruning, knowledge distillation, or inference optimisation is highly desirable.

• Experience deploying AI models on edge devices, embedded platforms, robots, or physical testbeds is highly desirable.

• Working knowledge of C/C++ is preferred.

• Strong research, experimentation, analytical, and scientific communication skills.

Preferred Research Experience

Candidates with experience in any of the following areas are particularly encouraged to apply:

Physical AI / Embodied AI | Robotics | VLM / VLA | Multimodal Foundation Models | Computer Vision | Edge AI | Model Optimisation | Sim-to-Real | Robot Learning | Real-Time AI Inference

What You Will Work On

The research pipeline will typically span:

Foundation Models → Multimodal Perception & Reasoning → Action Generation → Model Optimisation → Edge Deployment → Physical Testbed Validation

The role is primarily focused on AI algorithms, model research, optimisation, and edge deployment, rather than semiconductor IC design or hardware architecture development.