Engineer, Robotics Algorithm (SLAM & LLM)

Black Sesame Technologies (Singapore) Pte Ltd; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Black Sesame Technologies (Singapore) Pte Ltd in Singapore is seeking a Robotics Algorithm Engineer to develop intelligent navigation and perception algorithms for autonomous robots operating in complex environments.

You will focus on SLAM/autonomous navigation and LLM/VLM-based robotics intelligence, integrating with ROS 2 and other robotic platforms, and deploying solutions on real robots with hands-on implementation and strong problem-solving skills.

Qualifications

• Master’s degree or above in Robotics, Computer Science, AI, Automation, Electrical Engineering, or related fields.
• Strong knowledge of SLAM, localization, mapping, or autonomous navigation.
• Experience with one or more SLAM frameworks or algorithms (FAST-LIO2/FAST-LIVO2, LIO-SAM, ORB-SLAM, RTAB-Map, ICP/GICP/NDT, GTSAM).
• Experience with ROS/ROS 2, C++ and Python.
• Understanding of LLM, VLM, multimodal models, or AI Agent technologies.
• Experience applying LLM/VLM to robotics, spatial intelligence, navigation, or embodied AI is highly desirable.
• Strong problem-solving and hands-on implementation skills.

Responsibilities

• Develop and optimize LiDAR/ Vision/ IMU-based SLAM and localization algorithms.
• Develop robust navigation solutions for dynamic and GPS-denied construction environments.
• Work on point cloud registration, mapping, re-localization, loop closure, and map updating.
• Integrate SLAM and navigation with ROS 2 and robotic platforms.
• Develop LLM/VLM-based algorithms for robot scene understanding, task planning, and inspection.
• Develop methods to combine BIM, spatial information, robot perception, and LLM/VLM for various applications.
• Explore LLM/VLM-based agents, spatial reasoning, semantic mapping, and autonomous task generation.
• Conduct algorithm testing, system integration, and deployment on real robots.

Skills

SLAM

Autonomous navigation

Localization

C++

Python

ROS 2

LLM/VLM integration

AI Agent technologies

Education

Master’s degree or above in Robotics, Computer Science, AI, Automation, Electrical Engineering, or related fields

Tools

FAST-LIO2

FAST-LIVO2

LIO-SAM

ORB-SLAM

RTAB-Map

ICP/GICP/NDT

GTSAM

Job description

We are looking for a Robotics Algorithm Engineer to develop intelligent navigation and perception algorithms for autonomous robots operating in complex environments.

The role will focus on two key areas: SLAM/ autonomous navigation and LLM/VLM-based robotics intelligence, including spatial understanding, task reasoning, and BIM-aware robotic applications.

Key Responsibilities

• Develop and optimize LiDAR/ Vision/ IMU-based SLAM and localization algorithms.
• Develop robust navigation solutions for dynamic and GPS-denied construction environments.
• Work on point cloud registration, mapping, re-localization, loop closure, and map updating.
• Integrate SLAM and navigation with ROS 2 and robotic platforms.
• Develop LLM/VLM-based algorithms for robot scene understanding, task planning, and intelligent inspection.
• Develop methods to combine BIM, spatial information, robot perception, and LLM/VLM for various applications.
• Explore LLM/VLM-based agents, spatial reasoning, semantic mapping, and autonomous task generation.
• Conduct algorithm testing, system integration, and deployment on real robots.

Requirements

• Master’s degree or above in Robotics, Computer Science, AI, Automation, Electrical Engineering, or related fields.
• Strong knowledge of SLAM, localization, mapping, or autonomous navigation.
• Experience with one or more SLAM frameworks or algorithms, such as FAST-LIO2, FAST-LIVO2, LIO-SAM, ORB-SLAM, RTAB-Map, ICP/GICP/NDT, or GTSAM.
• Experience with ROS/ ROS 2, C++ and Python.
• Understanding of LLM, VLM, multimodal models, or AI Agent technologies.
• Experience applying LLM/VLM to robotics, spatial intelligence, navigation, or embodied AI is highly desirable.
• Strong problem-solving and hands-on implementation skills.

Experience with real-world robot deployment, publications, patents, or open-source robotics projects will be an advantage.