PhD Intern: Low-Power Sensing & AI Systems

ByteDance; San Jose (CA); On-site; USD 62,508 - 89,027; Part time

Benefits offered by this job

Health insurance

Housing allowance

Paid holidays

Paid sick time

Job summary

ByteDance invites PhD candidates to join the Intelligent Sensing Intern program in San Jose for 2027 Start. The project focuses on low-power, high-quality sensing for AR/VR cameras, integrating sensors, image pipelines, and AI models to push energy‑efficient perception.

Expect hands-on hardware‑software collaboration within a dynamic research team. The internship emphasizes cross‑disciplinary work across sensing, processing, and AI agents, with opportunities to contribute to system‑level

Qualifications

• PhD candidate in Electrical Engineering, Physics, Computer Engineering, AI, or a related discipline.
• Strong foundation in imaging, sensor tech, or machine learning / computer vision.
• Hands-on experience with VLM/LLM, world models, or large-scale vision models.

Responsibilities

• Design and prototype sensor/imaging architectures with near-sensor processing or learned compression.
• Characterize end-to-end imaging pipelines from optics to downstream perception models.
• Bridge hardware and algorithm thinking from device level to training workflows.
• Develop or adapt vision models co-optimized with hardware constraints.

Skills

Imaging systems

Sensor technology

Machine learning / CV

VLM/LLM experience

Research publications

Communication in teams

Education

PhD candidate in EE/Physics/CE/AI

Job description

ByteDance invites PhD candidates to join the Intelligent Sensing Intern program in San Jose for 2027 Start. The project focuses on low-power, high-quality sensing for AR/VR cameras, integrating sensors, image pipelines, and AI models to push energy‑efficient perception.

Expect hands-on hardware‑software collaboration within a dynamic research team. The internship emphasizes cross‑disciplinary work across sensing, processing, and AI agents, with opportunities to contribute to system‑level