Robot Learning Engineer

Dexmate; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

Dexmate in Singapore is seeking a Robotics Engineer to develop and deploy end-to-end learned models for robot manipulation and control. You will shape foundational systems that integrate multimodal data processing and policy deployment to physical robots.

This role requires a Master's or PhD in Robotics, ML, or CS, along with practical experience in training ML models, particularly in PyTorch. The position offers an opportunity to contribute to groundbreaking work in physical AI and robotics.

Qualifications

• Master's or PhD in Robotics, ML, CS, or related field.
• Hands-on experience training and deploying ML models, ideally in PyTorch.
• Background in robot learning, imitation/reinforcement learning, or learning-augmented control.

Responsibilities

• Design, train, and evaluate learned policies for manipulation and control.
• Deploy and validate models on physical robots, tuning for performance.
• Build infrastructure for model training: job scheduling, checkpointing, logging.

Skills

Training and deploying ML models in PyTorch

Imitation and reinforcement learning

Robot learning

Education

Master's or PhD in Robotics, ML, CS, or related field

Tools

PyTorch

Protobuf

Job description

Dexmate is building the foundation for physical AI — a unified platform that combines high-quality robotic hardware 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.

Responsibilities

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.

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