Job VC

We are looking for a Computer Vision Engineer with a solid foundation in classical computer vision and hands-on experience implementing low-level CV algorithms. The ideal candidate will have exposure to SLAM, Visual-Inertial Odometry (VIO), and sensor fusion, with responsibilities adjusted according to experience level.
Responsibilities:
Design, implement, and optimize classical CV algorithms independently
Develop and maintain components of SLAM/VIO pipelines
Contribute to sensor fusion modules involving visual and inertial data
Lead experiments, evaluate model performance, and propose improvements
Review research papers and translate findings into practical solutions
Collaborate across teams to ensure smooth integration of CV components.
Required Qualifications:
Strong practical experience with classical CV and geometric vision
Hands-on experience developing components of SLAM and/or VIO systems
Proficiency in C++ and confident working in Linux environments
Solid understanding of multi-sensor systems and data fusion
Proven ability to prototype, benchmark, and optimize CV algorithms
Ability to analyze research papers and implement state-of-the-art methods.
Nice to Have:
Experience with Python
Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
Experience with sensor fusion techniques
Familiarity with embedded or real-time systems