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Machine Learning System Hardware Architect
Machine Learning System Hardware Architect
Baidu is seeking a Machine Learning System Hardware Architect to join their SoC team in Sunnyvale, CA. This role involves architecting high-performance machine learning silicon and connecting Kunlun Accelerators for distributed AI training, while also driving architectural innovations and evaluating system-level ML architectures for performance, power, and latency.
About the role
About the Role
We are looking for a world-class Machine Learning System Architect (HW) to join our SoC team at Baidu’s Sunnyvale office. The successful candidate will be a motivated self-starter who will thrive in this highly technical environment. Your job responsibilities as a Machine Learning System Architect will help the team to architect and create high-performance machine learning silicon and connect thousands of Kunlun Accelerators together for distributed AI training tasks.
Responsibilities
- Create differentiated architectural innovations for Baidu’s Kunlun AI SoC roadmap.
- Architect, simulate, and design amazing machine learning solutions for our AI machine learning products.
- Develop system-level ML architectures that push the boundaries of performance, power, and latency; collaborate closely with many other teammates to ensure we design and optimize hardware and software for maximum performance.
- Monitor industrial and academic trends in artificial intelligence and determine where they should intersect our roadmaps.
- Drive partnerships for access to the most advanced AI technologies.
- Evaluate the power, performance, and cost of prospective architecture and subsystems.
- Build scalable tools for modeling and performance evaluation.
- Engage with system and application software engineers to ensure optimization of the entire hardware/software stack.
- Engage with SoC design, verification, and validation engineers to realize the architecture.
Qualifications
- Knowledge of Machine Learning market, technological and business trends, software ecosystem, and emerging applications.
- Proven track record 5+ years architecting hardware solutions for Machine Learning, acceleration and optimization.
- Experience with deep learning frameworks including TensorFlow, PyTorch, PaddlePaddle, etc.
- Strong track record of outreach to ML researchers and application developers.
- Experience with CPUs, GPUs, memory systems, and accelerators.
- Experience with performance simulation and modeling in C++.
- Experience with SoC interconnects and NoCs.
- Experience with area, frequency, and power optimizations.
- Familiarity with video, DSP, Ethernet, and PCIe.
- MS or PhD in Electrical or Computer Engineering.
- Excellent communication skills in both English and Chinese.