Description

Drive research and implementation of AI/ML/SL algorithms and systems focused on Large Foundation Models, AI for Science, Design Space Exploration (DSE), and Inverse Design.

Responsibilities

• Implement leading-edge Proof of Concepts using open-source technologies and best practices.
• Collaborate with academic and in-house researchers on reinforcement learning applications for computing, micro-architecture, chip, device, and materials design.
• Integrate design space exploration (DSE) and inverse design methodologies into practical design pipelines.
• Communicate complex research results and testing data through technical writing and oral presentations.
• Lead the development of technical papers and patents as core deliverables.

Required Skills

• PhD in Electrical Engineering, Computer Science, or a related field.
• 5+ years of experience in innovative and algorithmic software development.
• Deep knowledge of machine learning, statistical learning, and reinforcement learning.
• Proficiency in at least two programming languages, specifically Python and C/C++.
• Hands-on experience with PyTorch, TensorFlow, or similar statistical learning and auto-differentiation platforms.
• Strong background in research, software, and system engineering.
• Proven ability to troubleshoot technical problems arising in research involving open-source technologies.
• Experience working productively with open-source research software.

Preferred Skills

• Experience with multi-processing libraries and Python-based machine learning.
• Experience working in Linux and Docker environments.

Key Skills

Education

Any Graduate