AI Systems Engineer 24002

• Develop and Analyze Machine Learning or AI Models for Edge AI applications.
• Analyze OpenSource Large Language Vision and Multimodal models and deploy them for mobile systems.
• Customize the models for performance and power analysis and implement changes for performance improvements.
• Work with the thirdparty Application and Model developers to get custom models for mobile devices and to analyze Memory and Storage requirements.
• Work with the SDKs from SoC vendors to deploy models on different Accelerators in the mobile system and analyze the performance with different accelerators.
• Run tests to assess and AI performance.
• Stay up to date on the latest innovations in machine learning.

• Must have a BS or Master s degree or PhD in Electrical and/or Computer Engineering or Computer Science
• At least 5 years of relevant work experience.
• Strong understanding of dimensionality reduction techniques clustering and classification.
• Familiarity with supervised and unsupervised machine learning metrics and requirements.
• Excellent writing documentation and communication skills.
• Ability to learn new skills and take on new responsibilities.
• Experience working with AI Accelerators and Performance Analysis
• Experience in ASIC or FPGA Design Verification
• Experience in Architecture Microarchitecture of high speed low power optimal area IPs
• Great communications skills with the ability to articulate results.

• Experience with dynamical systems optimization and simulation.
• Knowledge of the latest and most performant ML/AI algorithms.
• Ability to create customer deliverable research reports.
• Proposal writing skills for new ML/AI work.
• Processor system knowledge including basic understanding of SoC systems as well as operating system software.

documentation,proposal writing,multi-modal models,unsupervised learning,sdks,ai,operating system software,communication skills,research reports,performance analysis,microarchitecture,supervised learning,classification,verification,dimensionality reduction techniques,ai/ml models,machine learning,mobile,clustering,dynamical systems,soc vendors,edge ai applications,asic,optimization,soc systems,ai accelerators,fpga design,vision,simulation,ml/ai algorithms,architecture,storage,open-source large language,memory