Physical AI Engineer

ST Engineering; Singapore; On-site; SGD 72,000 - 90,000; Full time

Job summary

A leading technology firm in Singapore is seeking a Physical AI Engineer to develop and integrate advanced AI models that enable real robots to autonomously perceive and act. The ideal candidate should have a strong background in AI/ML, robotics middleware, and hands-on experience in deploying models on embedded systems. Responsibilities include model development, fine-tuning, and optimizing for edge deployment, with an opportunity to work on cutting-edge robotics solutions in a high-impact environment.

Benefits

Opportunity to work on state-of-the-art AI models

Combination of research and deployment

High-impact work on cutting-edge technologies

Qualifications

• 3+ years of experience in robotics/Physical AI systems preferred.
• Strong foundation in robotics middleware.
• Good knowledge of model compression and acceleration techniques.

Responsibilities

• Develop and integrate AI/ML models for robotics applications.
• Fine-tune Generative/Foundation models for edge deployment.
• Build simulation data pipelines for model improvement.

Skills

AI/ML model development

ROS2 integration

Robotics middleware build

Model tuning and optimization

Embedded AI knowledge

Education

Bachelor’s/Master’s in Computer Science or related field

Tools

Jetson

Isaac Sim

TensorRT

Job description

We are looking for Physical AI Engineers focused on developing and integrating Machine Learning (including Generative AI) models that enable real robots to perceive, reason, and act autonomously. This role bridges Robotics and Agentic AI — working on perception, decision-making, and multi-model integration for multi-robot and embodied systems.

You will work on training and adapting Generative/Foundation models (vision, language, planning, VLA models, swarm reasoning) and deploying them onto edge and robotic hardware.

Responsibilities

• Develop and integrate AI/ML and Generative/Foundation models for perception, mapping, task planning, and embodied decision-making.

• Fine-tune or adapt Generative/Foundation models (Small Language Model/ Small-Vision-Language/ Vision-Language-Action) models for edge deployment for real-world robotic applications.

• Implement on-device model optimization (quantization, distillation, TensorRT, etc.).

• Build data pipelines: simulation → real-world → feedback loop for continual model improvement.

• Conduct domain adaptation and reinforcement/interactive learning for robot skills.

• Integrating models into ROS2 and swarm autonomy stacks.

• Evaluate model performance in both simulation and hardware deployment.

• Optimize inference latency and reliability on edge compute platforms.

• Formulate the conceptual and detailed technical solution for the development of applications to meet customer requirements.

• Provide recommendations on relevant emerging technology in Physical AI/Robotics to senior management.

• Identifying and leading strategic technical capability development for Physical AI/Robotics.

• Collaborate on research and development projects to explore new capabilities and applications for Physical AI/Robotics technology.

Minimum Requirements

• Bachelor’s/Master’s in Computer Science, Machine Learning, AI, Robotics, or related field.

• 3years of experience building robotics/Physical AI systems. Candidates without work experience but with relevant skills are also welcome to apply.

• Strong foundation in ROS2 or robotics middleware integration.

• Extensive experience building/fine-tuning of AI/ML and Generative/Foundation models (vision, transformer-based, or RL) for robotic systems.

• Good knowledge of model compression/acceleration for embedded devices (Jetson, Pi, etc).

• GPU/CUDA experience or on-device inference for embedded AI.

• Experienced with deployment on embedded/edge Linux systems.

• Good understanding of robotics model training pipelines (perception → planning → control).

Preferred Experience

• Vision-Language-Action (VLA) or Multi-Modal model experience.

• Reinforcement learning / imitation learning / interactive training loops.

• Synthetic data and simulation-based training (Isaac Sim, Habitat, etc.).

• Knowledge of distributed training pipelines or MLOps for robotics.

Additional Skills

• Strong experimentation and iterative problem-solving mindset.

• Comfortable bridging research prototypes into production-grade systems.

• Able to collaborate tightly with AI and systems engineers.

• Curious and self-driven — able to explore new Physical AI approaches and rapidly test them.

What We Offer

• Opportunity to work on state-of-the-art embodied AI models powering real robots.

• Combination of research and deployment — not just writing models, but seeing them act in the physical world.

• High-impact work on cutting-edge robotic autonomy and swarm behaviours.