Physical AI Engineer

ST ENGINEERING IHQ PTE. LTD.; Singapore; On-site; SGD 80,000 - 100,000; Full time

Job summary

A leading robotics company in Singapore is seeking Physical AI Engineers to develop and integrate advanced machine learning models for real robots. The role involves optimizing AI models for edge deployment and collaborating on innovative research projects. Candidates should hold a relevant degree and possess extensive experience in robotics systems and AI integration. Opportunities to work on groundbreaking applications in Physical AI are available, alongside a high-impact environment focused on cutting-edge technologies.

Benefits

Opportunity to work on cutting-edge AI models

Combination of research and deployment

High-impact projects in robotic autonomy

Qualifications

• 3 years of experience building robotics or Physical AI systems.
• Strong foundation in ROS2 or robotics middleware integration.
• Extensive experience with AI/ML and Generative/Foundation models.

Responsibilities

• Develop and integrate AI/ML and Generative/Foundation models for robotics.
• Optimize model performance for edge deployment.
• Collaborate on R&D projects for new capabilities in Physical AI.

Skills

Robotics integration

Generative AI models

Machine Learning

ROS2

Model optimization

Embedded systems

AI/ML system development

Data pipelines

Education

Bachelor’s/Master’s in Computer Science, Machine Learning, AI, Robotics

Tools

CUDA

Jetson

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) 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.
• Integrate 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.
• Identify and lead 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.
• 3 years 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 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.