Robotics | Applied AI Engineer | Physical Systems

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

Fast paced, open & collaborative environment! about the company

Our client is an innovative technology lab operating at the cutting edge of advanced automation and applied artificial intelligence. They are developing next-generation intelligent systems designed to transform physical labor on a massive scale. This is a highly dynamic environment where rapid iteration is prioritized, allowing engineers to see their software directly drive real-world physical capabilities within days.

about the role

You will serve as a critical engineer at the intersection of machine learning and physical execution. Your core focus will be translating complex AI models into tangible, real-world actions.

• Design and train sophisticated machine learning policies for dynamic, real-world control systems.

• Conduct hands‑on experiments to systematically debug and close the gap between simulated environments and physical reality.

• Build and maintain high‑speed data pipelines and simulation environments that enable rapid iteration.

• Analyze system performance during deployments to drive continuous improvements back into the training loop.

• Collaborate closely with cross‑functional engineering teams to integrate software with complex physical constraints.

skills and experience

• 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.

• Bonus: A background in control theory, dynamics, or experience with domain adaptation and multi‑axis physical systems.