System Engineer, Machine Learning

Sinian team focuses on heterogeneous compute and software-hardware cooperative technologies. We have worked on a unified heterogeneity-aware lowering and optimization platform, accelerating applications on various heterogeneous hardware. Our goal is to unleash the hardware computing power and deploy deep learning applications for improving portability, performance, and utilization.Your responsibilities include, but are not limited to:Installation, configuration and bring-up of vendor machine learning hardwareProviding operational support for prototype hardware and software system including validation and troubleshootingTroubleshoot and resolve any system-related issues arising during model training and deploymentPerformance analysis, profiling and benchmarking of machine learning workloads running on systemCollaborate with production team to distill the requirementsIndependently solving complex technical and logistical problems in a fast-paced environmentRequired:BS, MS, or Ph.D. in Computer Science, Computer Engineering, or related field;At least 3-5 years industry experience or relevant experience;Experience with ML Architectures and hardware accelerators, e.g. Nvidia GPU;Experience in machine learning frameworks and deep learning toolsets;Ability to work independently, good communication and strong interpersonal skills;Reliability and self-motivation in a dynamic product-oriented team;Desirable:Knowledge of CPUGPU architecture;Experience in large scale machine learning distributed training process;Knowledge of deep learning model algorithm and architecture;The pay range for this position at commencement of employment is expected to be between $156,000 and $256,800year. However, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience.If hired, employee will be in an "at-will position" and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual departmentteam performance, and market factors.

Created: 2024-11-06