Physical AI (Edge Intelligence) Research Scientist

Physical AI (Edge Intelligence) Research ScientistAbout the RoleWe are seeking a Physical AI (Edge Intelligence) Research Scientist to advance research at the intersection of Physical AI, Embodied AI, Multimodal AI, Foundation Models, Robotics, and Edge Intelligence.The role focuses on developing AI models that can perceive, reason, and generate actions in real-world physical environments, with an emphasis on efficient, robust, and low-latency AI inference on edge systems.You will bridge the gap between AI research and real-world deployment, working closely with AI researchers and hardware acceleration engineers to develop novel algorithms, optimise AI models, and validate them on physical edge platforms and testbeds.Key ResponsibilitiesConduct research in 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.Develop AI algorithms for perception, reasoning, decision-making, and action generation using multimodal sensory inputs.Optimise AI models for efficient and low-latency edge inference, including model compression, quantisation, pruning, knowledge distillation, and computational graph optimisation.Deploy and benchmark AI models on edge devices, embedded platforms, robots, and physical testbeds.Integrate vision feeds and multimodal sensor inputs into real-time AI inference pipelines.Utilise simulation environments for synthetic data generation, pre-training, domain adaptation, and sim-to-real evaluation.Collaborate with hardware and systems engineers to optimise AI workloads for target edge platforms.Develop and optimise AI inference pipelines using PyTorch, ONNX, TensorRT, and related technologies.Conduct experiments, benchmark model performance, analyse results, and translate research concepts into working prototypes.Publish research findings in leading AI/ML, computer vision, robotics, and related conferences or journals.Identify opportunities for technical patents and intellectual property.QualificationsPh.D. in Computer Science, Artificial Intelligence, Computer Vision, Robotics, Electrical Engineering, or a related quantitative discipline. 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 areas:Physical AI / Embodied AIRobotics / Robot LearningMultimodal AIVision-Language Models (VLMs)Vision-Language-Action (VLA) ModelsFoundation ModelsComputer VisionEdge AIStrong 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 model deployment and optimisation frameworks such as ONNX, TensorRT, CUDA, or similar technologies.Experience with model compression, quantisation, pruning, knowledge distillation, or inference optimisation is highly desirable.Experience deploying AI models on edge devices, embedded systems, robots, or physical testbeds is highly desirable.Working knowledge of C/C++ is preferred.Strong research, experimentation, analytical, and scientific communication skills.Preferred Technical ExperienceExperience in any of the following areas would be highly valued:Physical AI | Embodied AI | VLA | VLM | Multimodal AI | Robotics | Robot Learning | Computer Vision | Edge AI | AI Inference | Model Optimisation | Quantisation | TensorRT | ONNX | Sim-to-Real | Sensor Fusion | Real-Time AIResearch FocusThe role covers the following research pipeline:Foundation Models → Multimodal Perception → Reasoning & Decision Making → Action Generation → Model Optimisation → Edge Deployment → Physical Testbed ValidationThe position is primarily focused on AI research, algorithm development, model optimisation, and edge deployment, rather than traditional telecommunications or semiconductor IC design.