TCAD Machine Learning Engineer

Join Intel and build a better tomorrow. Intel is during an exciting transformation, with a vision to create and extend computing technology to connect and enrich the lives of every person on Earth. So, join us and help us create the next generation of technologies that will shape the future for decades to come.

Are you an engineer/physicist who loves programming? Do you enjoy creating machine learning models so that you can run them through a computer to answer questions that cannot be answered otherwise? Do you take pride in optimizing codes to reduce runtime? If your answer is yes, come join our team - we are looking for you. We are a team of software developers with strong engineering and physics background responsible for creating physics-based simulation and machine learning tools that are essential for developing next generation, angstrom-era Intel transistors.

• Responsibilities include, but are not limited to: Developing and training new machine learning models and improving existing ones
• Developing new capabilities to existing ML based software solutions, debugging and fixing bugs, and writing testcases
• Collaborating directly with the applications engineers in the US and other geographies to provide support and address feature requests
• Converting projects from proof-of-concept stage to production ready
• Maintain and develop software infrastructure
• Creating software simulation flows, developing physical models, and analyzing the fundamental device and process issues encountered in cutting-edge technology development
• Collaborating directly with the experimental research, process, and design teams to define experiments and present recommendations that achieve the scaling path for the logic, memory, and analog devices in advanced-node, industry-leading products
• What we offer: We give you opportunities to transform technology and create a better future, by delivering products that touch the lives of every person on earth
• As a global leader in innovation and new technology, we foster a collaborative, supportive, and exciting environment where the brightest minds in the world come together to achieve exceptional results
• We provide benefits that promote a healthy, enjoyable life: excellent medical plans, wellness programs, and amenities, time off, recreational activities, discounts on various products and services, and much more creative perks that make Intel a Great Place to Work We're constantly working on making a more connected and intelligent future, and we need your help

You must possess the minimum qualifications below to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. The experience listed below may be obtained through schoolwork, classes and project work, internships, military training, and/or work experience.

Minimum Qualifications:

• Candidate must be pursuing or recently completed an PhD in Electrical Engineering, Physics, Mechanical Engineering, Chemical Engineering, Mathematics, Computer Science and Engineering, Physics, Mathematics, or other relevant related degrees with a software focus
• A minimum of 1 years of research/thesis experience in building machine learning / deep learning models
• A minimum of 1 years of experience in software development through academic projects and/or research
• Proficient in Python, C/C++, or any other similar programming languages
• Familiar with backend and front-end web application development

Preferred Qualifications:

• A strong background in data structure and algorithms, and software development
• A strong background in statistics, linear algebra and solid-state device physics
• 2+ years of experience in developing full-stack web applications using frameworks like Flask, Django, Angular, and React
• 2+ years of experience in software development in high performance computing, computation modeling and numerical methods
• Publications and/or open-source projects that demonstrate expertise in areas such as machine learning, device physics or process flow physics