Research Engineer: Robotics Agent Singapore / Munich, Germany →

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

Job summary

MindOn is building general-purpose intelligence for robots and seeks a Research Engineer to design, develop, and deploy robotics agents on real systems.

You will combine LLMs, VLMs, and robot skills to translate user intent into executable tasks, handle environment changes, recover from failures, and progress toward goals.

Qualifications

• Bachelor's degree or higher in CS/Robotics/AI/Automation or related field.
• Proficiency with LLMs and VLMs and practical deployment experience.
• Strong Python and software engineering skills, incl. async tasks and testing.
• Experience designing reliable tool interfaces and execution workflows.
• Familiarity with core agent mechanisms: planning, memory, and context management.

Responsibilities

• Design robotics agent architectures linking perception, planning, and execution feedback.
• Use LLMs and VLMs for task decomposition and multi-step planning.
• Create tool-calling and skill interfaces for perception, navigation, and manipulation.
• Build execution state management, failure handling, retries, and replanning.
• Develop memory and context management for long-horizon tasks.
• Collaborate with robotics teams to deploy agents on real robots.
• Implement evaluation workflows to measure success, planning quality, latency, and failure causes.

Skills

Embodied AI

Python

Asynchronous programming

Testing and debugging

Cross-functional collaboration

System design

Experimentation and evaluation

Troubleshooting

Memory and context management

Education

Bachelor's degree or above in Computer Science, Robotics, AI, Automation, or related field

Tools

ROS/ROS2

Tool-calling interfaces

Job description

MindOn is building general-purpose intelligence for robots. We are looking for a Research Engineer to design, develop, and deploy robotics agents on real systems.

You will combine large language models (LLMs), vision-language models (VLMs), and robot skills to translate user intent into executable tasks, understand changes in the environment, recover from failures, and work toward completing goals.

Responsibilities

• Design and develop robotics agent architectures that connect task understanding, environment perception, task planning, skill invocation, and execution feedback.
• Use LLMs and VLMs for task decomposition and multi-step planning, translating natural language instructions into executable robot workflows.
• Design tool-calling and skill interfaces that connect perception, navigation, manipulation, and other system capabilities.
• Build execution state management and feedback mechanisms for progress tracking, failure detection, retries, and replanning.
• Develop context and memory management methods that organize environment information, interaction history, and execution results to support long-horizon tasks.
• Collaborate with robotics algorithm, control, and software teams to integrate and deploy agents on real robot systems.
• Build task-level evaluation workflows and analyze success rates, planning quality, response latency, and failure causes to continuously improve performance.

Qualifications

• A deep and genuine passion for Embodied AI is essential.
• Proficiency with AI coding tools is essential: use them effectively for development, debugging, testing, and troubleshooting; independently review generated code and validate its correctness and reliability through tests and real-world execution.
• A bachelor’s degree or above in Computer Science, Robotics, AI, Automation, or a related field, or equivalent practical experience.
• Familiarity with the principles, capabilities, limitations, and use of mainstream LLMs and VLMs, with experience integrating, deploying, or developing applications with these models.
• Understanding of core agent mechanisms, including tool calling, structured outputs, task planning, context management, and memory, with practical system development experience.
• Ability to design reliable tool interfaces and execution workflows that handle uncertain model outputs, failed calls, timeouts, and inconsistent state.
• Understanding of the relationship between robot task planning and low-level skill execution, with the ability to assess plan feasibility against environment state and skill constraints.
• Proficiency in Python and strong software engineering skills, including asynchronous tasks, state management, logging, and testing.
• Rigorous experimental and evaluation skills, using execution traces and failure cases to diagnose issues and validate improvements.
• Strong problem-solving skills, independent execution, and cross-functional collaboration.

Preferred Experience

• Experience with visually grounded planning, execution monitoring, or failure recovery.
• Familiarity with behavior trees, state machines, Task and Motion Planning (TAMP), or related methods.
• Familiarity with ROS/ROS2 and interface design for perception, navigation, or manipulation skills.
• Experience fine-tuning, deploying, or optimizing inference for LLMs or VLMs.
• Experience developing systems for long-horizon tasks, interactive task clarification, or human-robot collaboration.
• Relevant open-source projects or research contributions.