苹果Generative AI Engineer, Camera Algorithms
任职要求
Minimum Qualifications • BS and a minimum of 3 years of relevant industry experience • Deep understanding of and hands-on experience with modern Generative AI models (e.g., GANs, VAEs, Diffusion Models, Transformers) and fine-tuning techniques (e.g., LoRA). • Proficiency in ML frameworks such as PyTorch. • Strong programming skills in Python and/or C++. • Professional working proficiency in both Mandarin and English (written and verbal) is required for this role Preferred Qualifications • Proven track record of innovation in Generative AI for …
工作职责
• Design, train, fine-tune, and deploy generative models to solve practical challenges in image and video processing. • Collaborate with product and design teams to conceptualize and define new camera features powered by AI. • Partner with hardware, software, and firmware engineers to prototype, integrate, and optimize your algorithms for Apple’s unique silicon. • Engage directly with our silicon engineering teams to perform low-level neural network optimizations and influence future hardware design. • Drive projects from initial concept and research through to final integration and shipment in a future Apple product.
• Cooperate with cross functional and multidisciplinary teams to specify camera FMEA risk, develop and deploy calibration and performance validations stations to reduce camera product risks throughout the NPI cycle • Work with equipment vendors to design the perfect equipment from scratch for calibrating and verifying the performance of Apple products • Specify and acquire instruments, components and tools for use in an NPI environment • Run simulations or experiments to make data driven decisions and present results to senior management • Design, develop, debug and optimize algorithms to meet with accuracy and precision requirement for camera calibration and performance verification • Generate reports that analyze large amounts of NPI camera performance data, and present at both working level and executive level meetings • Up to 10% domestic and international traveling
• - Design, develop, optimize, review and debug software used in interacting with product, instrumentation and motion control systems. • - Specify and acquire instruments, components and tools for use in a NPI environment • - Collaborate with multi-functional teams to specify and implement test coverage • - Conduct HW/SW/FW validation and FA for testing metrology, instruments and systems by technical benchmarking (ex: Gauge R&R process) • - Run simulations or experiments to make data driven decisions and presenting results to senior management • - Deploy calibration and performance verification stations throughout the NPI cycle • - Generate reports that analyze large amounts of NPI camera/display performance data • - Debug devices from the system level down to the component level • - Redefine existing algorithms to optimize for performance or execution time • - Balance decisions between engineering asks and manufacturing efficiency while ensuring product quality
• Investigate and resolve sensor calibration and egomotion algorithm/toolchain issues across multiple OEM vehicle platforms. • Develop core autonomous driving functionality for global markets by fusing state-of-the-art perception DNNs with map signals. • Build real-time 3D world models for planning, integrating diverse inputs from sensors and external sources. • Develop and optimize LLM, VLM, and VLA systems for autonomous driving applications, including pre-training and fine-tuning. • Design innovative data generation and collection strategies to improve dataset diversity and quality. • Collaborate with cross-functional teams to deploy end-to-end AI models in production, ensuring performance, safety, and reliability standards are met.
- Research, design, and implement generative AI models for image and video restoration (e.g., deblurring, denoising, super-resolution, inpainting, frame interpolation). - Build and optimize training pipelines for large-scale datasets, including preprocessing, augmentation, and distributed training. - Evaluate restoration performance using both objective metrics (PSNR, SSIM, LPIPS) and subjective/perceptual quality measures. - Develop scalable and efficient inference pipelines, optimizing for latency, throughput, and memory. - Stay current with the latest research in computer vision and generative AI, and translate novel ideas into practical solutions. - Collaborate with cross-functional teams to integrate restoration models into production systems.