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Camera System Architect- PICO Lab VST - San Jose

单位:字节跳动类别:研发类型:社招地点:圣何塞更新:2026-09-24

岗位信息

招聘单位字节跳动
工作地点圣何塞
官方更新时间2026-02-10 01:19:58

职位描述

About team:

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.

Responsibilities

- Define and own the system architecture of end-to-end camera solutions, spanning optics, image sensors, compute platforms, mechanics, algorithms, and software.

- Drive hardware–software co-design to optimize system-level performance, power, cost, and scalability.

- Lead system-level imaging and perception performance definition, evaluation, and optimization across hardware and AI-based processing.

- Design and integrate computer vision and AI algorithms into production camera systems.

- Evaluate and integrate emerging imaging and AI technologies to maintain product competitiveness.

任职要求

Minimum Qualifications

- Master’s or PhD in Optical Engineering, Electrical Engineering, Computer Science, Computational Imaging, or related fields.

- 3+ years of experience in camera or imaging system development, with scope spanning both hardware and software.

- Proven full-cycle product development experience, including prototype builds, EVT, DVT, and mass production, in a hardware product environment.

- Leverage AI-assisted engineering tools (LLMs, VLMs) to accelerate design iteration, analysis, debugging, and technical decision-making.

- Strong system-level thinking, first-principles reasoning, and ability to make and defend complex technical trade-offs.

- Demonstrated ability to work effectively across cross-functional engineering teams.

Preferred Qualifications

- Familiarity with SoC / ISP / NPU architectures and model-to-hardware adaptation or optimization.

- Hands-on experience with computer vision and machine learning, particularly in real-time or resource-constrained systems.

- Prior ownership of camera system architecture or subsystem technical leadership.

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