Computer Vision Engineer

indigenous unmanned platforms, electro-optical payloads, onboard cameras, AI-enabled subsystems, and mission-critical in-house software

We are expanding our perception and autonomy engineering team to strengthen UAV navigation, vision intelligence, and real-time onboard processing.

Key Responsibilities

• Deploy and optimize

  AI/ML computer vision models

  on edge compute platforms such as

  NVIDIA Jetson (Nano/Xavier/Orin), Hailo, and other AI accelerators

  .
• Build and accelerate

  perception, mapping, and sensor-fusion pipelines

  using

  CUDA, TensorRT, GPU frameworks

  , and real-time optimizations for UAV flight environments.
• Develop, integrate, and fine-tune

  Visual Odometry (VO)

  and

  SLAM

  algorithms for autonomous navigation and GPS-denied operations.
• Work with various

  camera systems

  , including mono, stereo, gimbal-mounted EO sensors, and high-speed image pipelines used in UAV payloads.
• Collaborate with hardware, flight-control, and autonomy teams to ensure

  seamless integration

  of perception modules with UAV avionics.
• Manage real-time

  networking, data streaming, and low-latency communication

  between onboard compute and ground control systems.
• Perform system-level testing, tuning, and verification on actual UAV platforms.
• Contribute to in-house development of

  perception, mapping, and autonomy subsystems

  , aligned with VTOL's product roadmap.

Technical Skills

• Strong proficiency in

  GPU programming

  CUDA, TensorRT, OpenCL, or similar frameworks.
• Hands-on experience in deploying AI/ML vision models on

  edge hardware

  (Jetson/Orin/Hailo/Coral).
• Practical experience with

  VO/SLAM frameworks

  such as ORB-SLAM, VINS-Fusion, RTAB-Map, OKVIS, etc.
• Solid command over

  Python & C++

  for robotics and high-performance computing.
• Strong understanding of

  computer vision algorithms

  , camera calibration, multi-camera setups, and EO payload workflows.
• Experience with

  real-time communication protocols

  , data streaming, and onboard-to-ground data links.
• Basic working knowledge of

  ROS/ROS2

  , sensor drivers, and robotic middleware.

Educational Background

• Bachelor's/Master's degree in

  Robotics, Computer Engineering, Computer Vision, Electronics, or related disciplines

  .
• Experience of 2-3 years preferred.

Interested candidates may email their resume with cover letter and details of existing CTC, expected CTC and notice period to [HIDDEN TEXT]