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Camera System Architect - PICO Lab VST - San Jose

ByteDance
San Jose, USA
Full-time
Mid · 3+ years experience
Onsite
Discovered 1 months ago
LLMsVLMsSoCISPNPU

Full Job Posting

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.

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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