Applied AI Engineer for Robotics & Real-World Control

Randstad Singapore; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Randstad Singapore is seeking a hands-on ML engineer at the intersection of machine learning and physical execution. You will translate AI models into real-world actions for dynamic control systems in a fast-paced, collaborative environment.

You'll design and train policies for real-time control, conduct experiments to bridge simulation and hardware, build fast data pipelines, and work with cross‑functional teams to integrate software with complex physical constraints.

Qualifications

• Proven experience in applied machine learning, specifically focusing on reinforcement or imitation learning.
• Demonstrated ability to deploy models onto actual physical systems, moving beyond theoretical simulations.
• Advanced proficiency in Python and familiarity with modern ML or physics‑based frameworks.
• A highly empirical, debugging‑oriented mindset with a focus on practical results that work in the real world.
• Background in control theory, dynamics, or domain adaptation and multi‑axis physical systems (bonus).

Responsibilities

• Design and train sophisticated machine learning policies for dynamic, real-world control systems.
• Conduct hands‑on experiments to debug and close the gap between simulated environments and physical reality.
• Build and maintain high‑speed data pipelines and simulation environments for rapid iteration.
• Analyze system performance during deployments to drive continuous improvements in the training loop.
• Collaborate closely with cross‑functional engineering teams to integrate software with complex physical constraints.

Skills

Applied ML

Python

Deployment on physical systems

Debugging mindset

Control theory

Job description

Randstad Singapore is seeking a hands-on ML engineer at the intersection of machine learning and physical execution. You will translate AI models into real-world actions for dynamic control systems in a fast-paced, collaborative environment.

You'll design and train policies for real-time control, conduct experiments to bridge simulation and hardware, build fast data pipelines, and work with cross‑functional teams to integrate software with complex physical constraints.