Role Overview

VinRobotics is seeking Computer Vision Engineers to join our Humanoid Robotics Perception Team. You will design and deploy real-time perception systems enabling humanoid robots to see, localize, understand, and navigate complex real-world environments. This role focuses on 4 core perception pillars:

1. 6-DoF Object Pose Estimation for manipulation

2. Stereo Depth Estimation for geometry understanding

3. Semantic Segmentation & Mapping for navigation and scene understanding

4. Visual SLAM Your work will directly power grasping, manipulation, obstacle avoidance, semantic mapping, visual SLAM, and autonomous navigation on next-generation robotic platforms.

Key Responsibilities

6-DoF Object Pose Estimation (Manipulation Perception)

• Design and implement 6D object pose estimation pipelines using RGB, RGB-D, or stereo inputs -

• Handle occlusion, symmetry, cluttered scenes, and domain shift

• Integrate pose outputs with grasp planning and manipulation stacks

• Optimize inference pipelines for real-time robotic execution

Stereo Depth Estimation

• Develop and optimize stereo depth estimation pipelines

• Handle challenging conditions:

  1. Low texture

  2. Reflective / transparent surfaces

  3. Outdoor / indoor lighting variation

• Evaluate depth accuracy, completeness, and latency under real robotic constraints

Semantic Segmentation & Navigation Perception

• Build semantic segmentation models for:

  1. Travers ability

  2. Obstacle classification

  3. Scene understanding (floor, walls, objects, humans, dynamic agents)

• Contribute to semantic 3D mapping and semantic SLAM pipelines

• Support downstream modules such as:

  1. Local planning

  2. Obstacle avoidance

  3. Global navigation and relocalization

Sensor Fusion & System Integration

• Develop multi-camera perception systems (RGB, stereo, RGB-D)

• Integrate perception modules with ROS 2 Humble and real robot stack

• Collaborate with SLAM, control, and motion planning teams (MoveIt, Nav2)

• Ensure robust synchronization, calibration, and frame alignment

Visual SLAM

• Design, implement, and optimize Visual / Visual-Inertial SLAM pipelines for real-time robot localization and mapping.

• Integrate loop closure and place recognition to ensure long-term localization consistency.

• Fuse multi-sensor data (RGB, stereo, RGB-D, IMU) for improved accuracy and robustness.

• Optimize SLAM systems for low latency, high reliability, and real-time deployment on robotic platforms.

Technical Requirements

Core Skills (Required)

• Strong background in Computer Vision, Robotics, or Deep Learning

• Solid understanding of multi-view geometry, epipolar geometry, and camera models

• Hands-on experience with deep learning frameworks: o PyTorch / Tensorflow o ONNX / TensorRT o CUDA (deployment & optimization)

• Experience with 3D data processing: o Open3D, PCL o NumPy, PyTorch3D

• Proficiency in Python and/or C++ on Linux Robotics & System Experience

• ROS 2

• Experience with camera drivers & sensors: o Intel RealSense o ZED (stereo & RGB-D)

• Familiarity with MoveIt / Nav2 / robotic execution pipelines

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Vision, Robotics, AI, or related fields

• Experience in one or more of the following:

  1. Multi-view perception

  2. Visual SLAM / Visual-Inertial systems

  3. Robot grasp learning

  4. Semantic mapping or navigation perception

  5. GPU optimization

  6. Distributed training

  7. Synthetic-to-real domain adaptation

What We Offer

• Work on cutting-edge humanoid and autonomous robotics systems

• Real-world deployment on state-of-the-art robotic hardware

• Collaborative environment with AI researchers, roboticists, and system engineers

• Access to GPU clusters, simulation environments, and large-scale datasets

• Competitive compensation, benefits, and career growth opportunities