---
title: 'Robot Learning Engineer at Dexmate'
canonical: 'https://feeny.ai/job/robot-learning-engineer-dexmate-singapore-tffhfs6wmx3b'
type: 'job'
last_seen: '2026-10-18'
---

# Robot Learning Engineer at Dexmate

- **Company:** Dexmate
- **Location:** Singapore
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-06-29
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/dexmate/3be35ddb-570e-4d28-8f8e-ea81ae476e19/application

**Skills:** PyTorch, Robot Learning, Imitation Learning, Reinforcement Learning, Learning-Augmented Control, Model Deployment, Distributed Training, GPU Clusters, Video Generation Architectures, Robot Foundation Models, Protobuf, MCAP, ICRA, IROS, CoRL, RSS, NeurIPS, ICML

> The Robot Learning Engineer will design, train, and evaluate learned policies for general-purpose robot manipulation and control. This role involves building foundational systems for physical AI, including policy learning, training infrastructure, and real-time inference, while collaborating with hardware and contro...

## 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

## About Dexmate

## Company Overview
- **One-liner**: Dexmate builds general-purpose robots with manipulation capabilities, integrating custom hardware with AI models for robotic operation.
- **Entity Type**: Private (Seed Stage)
- **Headquarters**: Santa Clara, California, United States
- **Founded**: 2024
- **Founders**: Tao Chen (Co-Founder, CEO), Yuzhe Qin (Co-Founder, CTO), Chongyang Wang (Co-Founder)

## Core Business
- Primary industry/industries: Robotics Engineering, Artificial Intelligence, Automation
- Target customers: B2B (enterprise, logistics, manufacturing, and research organizations)
- Mission or purpose statement: "Building tomorrow's intelligence and automation."

## Products & Services
- **[Vega]**: A general-purpose mobile robot with high-payload arms, dexterous hands, a foldable torso, and an omnidirectional base. The robot is powered by custom-built hardware integrated with AI models for physical robotic operation.

## Market Standing
- **Valuation/Market Cap**: Not publicly available
- **Key Metric**: Total Funding raised via a Seed Round (closed ~March 2026) with participation from LG Technology Ventures as a lead investor.
- **Notable Investors/Partners**: LG Technology Ventures (lead investor), LG CNS (strategic partner for building Physical AI infrastructure for smart logistics).
- **Growth Signals**: Headcount grew +328.6% year-over-year (from ~4 to 18 employees). LinkedIn followers grew +67.8% yearly. In April 2026, launched the "Dexmate Build with Vega U Research Grant Program," selecting six research teams to receive a Vega robot. Strong hiring momentum with 11 active job postings as of mid-2026.

## Competitive Advantages
- **Integrated Hardware + AI Stack**: Designs custom high-payload arms, dexterous hands, and omnidirectional bases specifically for AI-driven manipulation, creating a tight hardware-software moat.
- **Physical AI Focus**: Differentiating by building "Physical AI" that moves beyond lab settings into real-world logistics and manufacturing, as evidenced by the partnership with LG CNS.
- **Rapid Scaling**: Demonstrated ability to grow headcount and attract top-tier talent from institutions like Carnegie Mellon University, UC San Diego, and companies like NVIDIA and Bosch.

## Strategic Focus
- **Physical AI Infrastructure**: Partnering with LG CNS to build the infrastructure that enables Physical AI to operate in smart logistics and manufacturing environments.
- **Research & Ecosystem**: Grant programs to seed the research community with Vega robots, driving platform adoption and innovation.
- **Talent Acquisition**: Aggressively hiring across engineering (AI, robotics, embedded systems), design, product management, and business development to scale the team.

## Why Work Here
- **Mission-Driven**: Opportunity to work on cutting-edge general-purpose robotics and Physical AI, moving technology from labs to real-world impact.
- **Engineering-Heavy Culture**: 60% of employees are in technical roles (AI, robotics, software, hardware), with a strong emphasis on hands-on building.
- **Fast-Growing Startup**: ~328% headcount growth in one year, indicating high trajectory and opportunity for early employees to have outsized impact.
- **Location**: HQ in Santa Clara, CA (Silicon Valley), with an in-office work policy (on-site workspace).
- **Compensation**: Engineering roles list salary ranges like $100K–$300K, competitive for the robotics/AI space.
- **Notable Culture Signals**: "Build with us" hiring language, active LinkedIn community (4,273 followers), and a focus on "smile, reach out, and instantly connect" suggests a collaborative, builder-oriented environment.

## Sources
1. [dexmate.ai](https://www.dexmate.ai/)
2. [jobs.ashbyhq.com/dexmate](https://jobs.ashbyhq.com/dexmate)
3. [builtin.com/company/dexmate](https://builtin.com/company/dexmate)
4. [craft.co/dexmate](https://craft.co/dexmate)
5. [linkedin.com/company/dexmate](https://www.linkedin.com/company/dexmate)

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