Physical AI Heterogeneous System Design Engineer

Starry Recruitment; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

Starry Recruitment is seeking a Physical AI Heterogeneous System Design Engineer to develop next-generation robotic systems that integrate AI, hardware acceleration, real-time control, and robotics.

You will work across heterogeneous GPU + FPGA platforms, integrating multimodal AI models with robotic control systems to enable intelligent and low-latency autonomous manipulation. This role requires Master’s or Ph.D. and hands-on experience with PyTorch/TensorFlow, ROS 2, HLS, and FPGA development.

Qualifications

• Master’s or Ph.D. in Electronic Engineering, Computer Engineering, Robotics, CS, or related field.
• Hands-on experience with PyTorch/TensorFlow, ONNX, and AI model optimization.
• Experience with ROS/ROS 2 and robotic motion-control systems.
• Strong understanding of multimodal AI architectures (CNNs, ViTs, LLMs, VLMs, VLA).
• 3+ years of hands-on HLS experience, hardware acceleration, FPGA-based development.
• Experience with timing closure, FPGA resource optimization, and HW/SW co-design.
• Experience with Physical AI, VLA models, robotic arms, simulation environments (MuJoCo, robosuite, Isaac Sim).
• Strong programming skills in C, C++, Python; embedded drivers and real-time apps.

Responsibilities

• Develop, train, and optimize Physical AI models using PyTorch, TensorFlow, ONNX workflows.
• Design and implement custom hardware accelerators using HLS with low latency and efficiency.
• Develop closed-loop robotic manipulation and motion-control systems using ROS 2.
• Integrate vision, force sensing, VLA model inference, actuation, and telemetry into prototypes.
• Implement real-time sensor and motion-control interfaces on FPGA platforms (PCIe, AXI4).
• Translate multimodal neural networks into C/C++ HLS and ROS 2 interfaces.
• Perform simulation, hardware testing, performance optimization, demonstrations.
• Collaborate across AI, robotics, embedded systems, and hardware teams from concept to deployment.

Skills

PyTorch/TensorFlow/ONNX

ROS/ROS 2

HLS/FPGA acceleration

C/C++/Python

Multimodal AI architectures

HW/SW co-design

Education

Master’s or Ph.D. in Electronic Engineering/Computer Engineering/Robotics/CS

Tools

HLS (Vivado HLS)

MuJoCo

robosuite

Isaac Sim

Job description

Physical AI Heterogeneous System Design Engineer

About the Role

We are seeking a Physical AI Heterogeneous System Design Engineer to develop next-generation robotic systems that combine AI, hardware acceleration, real-time control, and robotics. You will work across heterogeneous GPU + FPGA platforms, integrating multimodal AI models with robotic control systems to enable intelligent and low-latency autonomous manipulation.

Key Responsibilities

• Develop, train, and optimize Physical AI models using PyTorch, TensorFlow, and ONNX-based deployment workflows.
• Design and implement custom hardware accelerators using High-Level Synthesis (HLS), with a focus on low latency, resource efficiency, and timing closure.
• Develop closed-loop robotic manipulation and motion-control systems using ROS/ROS 2 and physics-based simulation environments.
• Integrate vision, force sensing, VLA model inference, robotic actuation, and system telemetry into end-to-end physical AI prototypes.
• Implement real-time sensor and motion-control interfaces on FPGA platforms, including PCIe and AXI4-based interconnects.
• Translate multimodal neural network architectures into C/C++ HLS implementations and ROS 2 middleware interfaces.
• Conduct simulation, hardware testing, performance optimization, and physical prototype demonstrations.
• Collaborate across AI, robotics, embedded systems, and hardware engineering teams to bring prototypes from concept to deployment.

Qualifications

• Master’s or Ph.D. in Electronic Engineering, Computer Engineering, Robotics, Computer Science, or a related field.
• Strong hands-on experience with PyTorch/TensorFlow, ONNX, and AI model optimization.
• Experience with ROS/ROS 2 and robotic motion-control systems.
• Strong understanding of multimodal AI architectures, including CNNs, ViTs, LLMs, VLMs, and VLA models.
• 3+ years of hands‑on HLS experience, including hardware acceleration and FPGA-based system development.
• Demonstrated experience with timing closure, FPGA resource optimization, and hardware/software co‑design.
• Experience with Physical AI, VLA models, robotic arms, and simulation/training environments such as MuJoCo, robosuite, or Isaac Sim.
• Strong programming skills in C, C++, and Python, including embedded drivers, hardware testbenches, and real-time applications.
• Experience integrating AI inference with sensors, actuators, and real‑time robotic systems is highly preferred.

Ideal Candidate

The ideal candidate combines expertise across AI/ML, FPGA acceleration, robotics, and real‑time systems and is comfortable moving from AI model development to hardware implementation and physical robot demonstration.