Robot Learning Engineer

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

Job summary

Dexmate is building the next layer of robot intelligence, offering a platform that streamlines learning-based control for real-world robots. This role focuses on designing and deploying learned policies across the full robot learning pipeline, from data ingestion to hardware deployment.

The ideal candidate has a strong background in robotics, machine learning, and a track record of moving research into deployed systems.

Qualifications

• Advanced degree in Robotics, ML, CS or equivalent practical experience.
• Hands-on ML model training and deployment, preferably with PyTorch.
• Background in robot learning, imitation or reinforcement learning.
• Experience taking models from research to deployment in real systems.

Responsibilities

• Design, train, and evaluate learned policies for manipulation and whole-body control.
• Deploy and validate models on physical robots, tuning for real-world performance.
• Iterate between simulation and real robots, collecting data and debugging failures.
• Own infrastructure for model training: scheduling, checkpoints, metrics, logging.
• Build tooling for debugging, visualization, and experiment analysis.
• Collaborate with controls, embedded, and hardware teams to deploy reliable systems.

Skills

Advanced degree

ML model training

Robot learning

Ownership mindset

Research-to-deployment

Education

Master's or PhD in Robotics/ML/CS

Tools

PyTorch

Protobuf/MCAP

Job description

ABOUT DEXMATE

Dexmate is building the foundation for physical AI — combining a new generation of robots with a universal Physical AI OS, making robots as easy to build and deploy as software. Today, robotics is fragmented, slow, and closed: most builders are forced to reinvent the same stack again and again, and most ideas never make it past the prototype stage. We exist to change that. Our mission is to democratize robotics by lowering the barrier to entry, delivering a plug-and-play platform for developers, researchers, and enterprises, and cultivating an open ecosystem that accelerates the evolution of physical AI.

If you want to help shape the next layer of human capability — and believe the future of robotics should be built together, not in isolation — we'd love to build it with you.

THE ROLE

We're developing end-to-end learned models for general-purpose robot manipulation and control, and you'll help build and shape the foundational systems that put capable robots into the real world. This is a broad role that can be tailored to your area of expertise — policy learning, training infrastructure, or real-time inference. You'll work across the full robot learning pipeline: ingesting and processing multimodal data, training large-scale models, deploying policies to physical hardware, and closing the loop between research and real-world performance.

RESPONSIBILITIES

ROBOT LEARNING & POLICY DEVELOPMENT

• Design, train, and evaluate learned policies for manipulation and whole-body control — imitation learning, reinforcement learning, and VLA-style architectures
• Deploy and validate models on physical robots, tuning for real-world performance and closing the loop between policy, controller, and hardware
• Iterate rapidly between simulation and real robots — design experiments, collect data, debug failure modes, and drive measurable improvements
• Own infrastructure for model training: job scheduling, checkpointing, metrics, and logging; scale distributed training across GPU clusters
• Build research tooling for debugging, visualization, and experiment analysis
• Work closely with controls, embedded, and hardware teams to translate research needs into reliable, deployable systems

MINIMUM QUALIFICATIONS

• Master's or PhD in Robotics, ML, CS, or related field, or equivalent practical experience
• Hands-on experience training and deploying ML models, ideally in PyTorch
• Background in robot learning, imitation/reinforcement learning, or learning-augmented control
• Experience taking models from research to deployment — not just offline benchmarks
• Ownership mindset: you design, build, operate, and iterate on systems end-to-end

PREFERRED QUALIFICATIONS

• Experience with VLAs, video generation architectures, or robot foundation models
• Experience deploying learned policies on physical robots — manipulation, legged, or humanoid systems
• Experience with serialization formats for high-performance systems (Protobuf, MCAP)
• Track record of publications at top-tier venues (ICRA, IROS, CoRL, RSS, NeurIPS, ICML) is a strong plus