XR Camera and Machine Vision Engineer Graduate (PICO) - 2026 Start (PhD)

About Team:
PICO is a leading VR/AR platform with independent innovation and R&D capabilities, focusing on VR all-in-one technology. PICO is committed to offering immersive and interactive VR experiences to our customers, including providing tailor-made solutions for our enterprise clients in the fields of education and healthcare.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities:

• Develop power & computational efficient machine vision perception systems.
• Camera system level HW and SW development.
• Design experiments and test platforms for visual performance evaluation.
• Optimize machine vision algorithm/framework for edge devices with efficient HW/SW architecture.
• Define specs for next generation VR/MR/AR cameras and see-through experiences.

Requirements:

• Minimum Qualifications

• Individuals who are completing or have recently completed a PhD in EE, ECE, CS, Applied Physics, or a related technical discipline.

• Direct experience with cameras / optical image sensors and computer vision on real images — CMOS/CIS, event cameras, computational imaging, or camera-based perception.

• Hands-on hardware: physically built and prototype real systems (PCB, FPGA, sensor/optical front-end bring-up, embedded).

• Modern ML/vision: research-level computer vision, efficient perception, or foundation models, with real understanding of what modern models need from the input data.

• Strong programming: Python and C/C++ (Matlab a plus).

• Proactive, self-motivated, strong communication in a fast-paced, highly multidisciplinary team.

• Preferred Qualifications

• Work at the hardware–algorithm intersection: sensor-algorithm co-design, near-/in-sensor compute, event cameras, computational imaging, efficient/edge perception.

• Fluency leveraging VLM/LLM/foundation models to build or accelerate perception systems.

• Low-power/edge deployment, power-performance modeling, or AI-accelerator-aware model design.

• 3D computer vision / reconstruction; image-processing pipeline development.