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Senior Software Engineer, Imaging, Machine Learning, Multimedia
3 weeks ago
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
- 5 years of experience with software development in C or C++.
- 3 years of experience working with embedded operating systems for mobile SoCs, and 1 year of experience with software design and architecture.
- 3 years of experience in system software development, including kernel and user mode drivers, HAL, or multimedia subsystems (Camera ISP, Tensor Processing Unit (TPU)/Neural Processing Unit (NPU), Video, Graphics Processing Unit (GPU), and Display).
- 1 year of experience with on-device Machine Learning.
- 5 years of experience with data structures and algorithms.
- Experience with GPU and DSP software development for imaging, computer vision and multimedia applications using programming languages (e.g., OpenCL, Vulkan, CUDA, Halide and architecture specific C/C++ intrinsics).
- Experience with mobile SoC architecture and multimedia subsystems (e.g., Camera ISP, TPU, GPU, Video Codecs, Display Engine and Security).
- Experience with ML inference pipeline development and optimization.
- Experience with system architecture analysis for imaging and multimedia user experiences to identify performance and power bottlenecks and areas for optimization.
- Experience with ML algorithms and tools, Large Language Models and Generative AI.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
We are the team that builds Google Tensor, Google's custom System-on-Chip (SoC) that powers the latest Pixel phones. Tensor makes transformative user experiences possible with the help of Machine Learning (ML) running on Tensor TPU. Our goal is to productize the latest innovations and research from Google by delivering computing hardware, software and experiences that delight users.
In this role, you will leverage the expertise in Imaging, Machine Learning (ML) and Multimedia software design and implementation to prototype proof-of-concept user experiences and use-cases for future Google Tensor SoC based Pixel devices, powered by the most advanced Google research in Generative AI and computational photography.
Responsibilities
- Contribute to imaging, machine learning and multimedia systems architecture and design for Google's Tensor SoCs and Pixel devices.
- Build prototype/production software across the camera, machine learning, and multimedia software stack including kernel and user mode drivers, Android HAL and framework, ML stack, and application to prove feasibility of new and emerging use-cases on future Tensor SoCs.
- Optimize power and performance of imaging and Generative AI use-cases running on-device.
- Collaborate with multimedia IP and architecture, system architecture and device software teams to take the prototypes into production.
- Perform detailed system analysis for complex imaging and Generative AI user experiences to identify performance and power bottlenecks and areas for optimizations, and communicate the analysis results in both quantitative and qualitative way.