Vision Systems Architect

Project Description:
• Owns the end-to-end vision and AI architecture for consumer devices. Defines scalable, power-efficient, and production-ready imaging and perception systems across product lines. Drives technical direction across sensor, ISP, CV, ML, and platform integration.

Skills required:
• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field (PhD preferred).
• 10-15+ years of experience in imaging/vision/perception systems.
• Proven experience architecting and shipping multiple consumer products with vision/AI.
• Deep understanding of imaging pipeline (sensor behavior, ISP, tuning impact).
• Strong embedded system architecture experience.
• Expert-level C++ and system-level optimization knowledge.
• Strong expertise in CV and ML system integration.
• Experience deploying optimized ML models on consumer SoCs.
• Experience with heterogeneous compute architectures.
• Experience defining system-level KPIs and validation strategies.
• Strong cross-functional leadership and communication skills.
Nice To Have:
• Experience with computational photography pipelines.
• Experience in AR/VR or advanced camera features.
• Experience with silicon bring-up and HW/SW co-design.
• Experience with large-scale model deployment and OTA updates.
• Experience in multi-camera or depth-sensing systems.
• Contributions to patents or technical publications.
• Experience leading architecture across multiple product generations.
Responsibilities:
• Define and own end-to-end vision system architecture (sensor → ISP → CV/ML → application layer).
• Translate product and UX requirements into scalable technical solutions.
• Define image quality strategy aligned with AI and user experience goals.
• Architect reusable, modular CV/ML frameworks across multiple product SKUs.
• Drive system-level trade-offs across performance, power, cost, and thermal limits.
• Define AI model lifecycle strategy from training to OTA deployment.
• Architect heterogeneous compute utilization (CPU/GPU/NPU/DSP).
• Define system KPIs: latency, FPS, power, memory, boot time, and robustness.
• Lead technology selection for inference engines, frameworks, and hardware platforms.
• Guide SoC bring-up and performance validation for vision pipelines.
• Establish coding standards, modularization strategy, and long-term maintainability.
• Identify technical risks early and define mitigation strategies.
• Collaborate with hardware, ISP, Android/Linux platform, and product teams.
• Mentor senior engineers and provide architectural governance.
• Contribute to long-term vision/AI roadmap across product generations.
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