Intelligent Sensing Intern - Low-power Agent Sensing and Computing Systems - Global Frontier Tech Recruitment Program - 2027 Start (PHD)

We are looking for talented individuals to join us for an internship in 2026.

PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis - we encourage you to apply early.

Please state your availability clearly in your resume (Start date, End date).

Team Introduction: Team Introduction: The VST/camera team focuses on finding low power, high image quality solutions for AR/VR camera systems, including developing CMOS sensors, novel optical/imaging sensors, ultra-compact camera module technologies, camera lens design, and algorithms.

The team is vertically integrating HW/SW for system optimization, shaping the AI experience for the future

Topic Content: The rapid advancement of AI Agents is set to have a far-reaching and transformative impact on daily life. As the hardware bridge linking AI Agents to the physical world and end users, intelligent hardware acts as a vital gateway for agents to perceive their environment and recognize user intent. Enabling AI Agents to maintain round‑the‑clock environmental awareness and efficiently capture real‑time user intentions is essential to improving their everyday service experience. This topic seeks to overcome the limitations of conventional visual perception systems. By deeply integrating sensing and computing, it explores full‑stack innovation spanning from low‑level hardware to high‑level algorithms. Key efforts include developing next‑generation sensors with real‑time understanding capabilities, investigating non‑traditional visual capture and compression techniques, and designing novel hardware architectures to run advanced algorithms—enabling highly efficient coordination across sensing, processing, and communication.

Topic Challenge: Break free from the framework of conventional sensing systems, explore novel sensors, signal processing and compression schemes to achieve highly energy-efficient sensing tasks, while enabling seamless integration with large models.

Topic Value: Breakthroughs in this research direction will enable intelligent hardware to better connect AI, users, and daily life.

Against the backdrop of the AI Agent era, this will unlock the gateway to the next generation of intelligent hardware terminals and open up broader technological possibilities.

Project Objective: Rethink visual perception from the sensor up.

Instead of treating sensing and computation as separate stages, we're exploring architectures that fuse them — sensors that compress, select, and partially "understand" the scene before data ever leaves the chip.

This requires deeply understanding both the hardware constraints and what modern vision-language and world models actually need from the sensing front-end.

The goal: order-of-magnitude gains in perception efficiency by co-designing hardware and algorithms across the full pipeline.

Responsibilities - Design and prototype novel sensor or imaging architectures that move computation closer to the sensing front-end (e.g., near-sensor processing, event-driven capture, learned compression at the pixel level) - Build and characterize imaging pipelines end-to-end: from optical/sensor physics through ISP to downstream perception models — identify where bits are wasted and where intelligence should be injected - Leverage understanding of VLM/LLM and world models to inform what information the sensing front-end must preserve, discard, or transform — closing the loop between foundation model requirements and hardware design - Develop or adapt machine vision models that are co-optimized with hardware constraints (power, bandwidth, latency)

Minimum Qualifications - PhD candidate in the field Electrical Engineering, Physics, Computer Engineering, AI, or a related discipline - Strong foundation in at least one of: imaging systems, sensor technology, or machine learning / computer vision - Hands-on experience with VLM/LLM, world models, or large-scale vision foundation models, including understanding of data and architectural requirements - Track record of high impact publication such as Nature, Science, CVPR, ICCV, ECCV, NeurIPS, SIGGRAPH, or equivalent - Ability to bridge hardware and algorithm thinking — from device-level concepts to model training workflows - Strong communication skills with demonstrated ability to collaborate effectively in teams.

Preferred Qualifications - Deep experience working across both hardware (e.g., sensors, imaging pipelines) and software/model development - Comfortable operating in ambiguous environments with an “explorer mindset” - Experience with AI-assisted development tools in research or engineering workflows - Multidisciplinary project experience and ability to quickly ramp in unfamiliar domains