Applied Robotics AI Engineer - Real-World Deployment

Randstad Singapore; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

Randstad Singapore represents an innovative technology lab pushing automation and AI frontiers. This role sits at the intersection of ML and physical execution, translating complex AI models into real-world actions in a fast, iterative environment.

You will design and train reinforcement or imitation learning policies for dynamic control, run hands-on experiments to close the sim-to-real gap, build high-speed data pipelines and simulators, and collaborate with cross‑functional teams to integrate

Qualifications

• Proven experience in applied machine learning, specifically reinforcement or imitation learning.
• Experience deploying models on actual physical systems, moving beyond simulations.
• Advanced proficiency in Python and familiarity with modern ML or physics-based frameworks.
• Empirical, debugging-oriented mindset with focus on practical results that work in the real world.
• Bonus: Background in control theory, dynamics, or domain adaptation and multi-axis physical systems.

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 training loops.
• Collaborate with cross-functional engineering teams to integrate software with complex physical constraints.

Skills

Applied machine learning

Reinforcement learning

Imitation learning

Empirical debugging mindset

Control theory (bonus)

Tools

Python

ML frameworks

Simulation environments

Job description

Randstad Singapore represents an innovative technology lab pushing automation and AI frontiers. This role sits at the intersection of ML and physical execution, translating complex AI models into real-world actions in a fast, iterative environment.

You will design and train reinforcement or imitation learning policies for dynamic control, run hands-on experiments to close the sim-to-real gap, build high-speed data pipelines and simulators, and collaborate with cross‑functional teams to integrate