Engineering Intern 1

The Group You’ll Be A Part Of

The Office of the CTO is where innovation takes center stage. We inspire our global technical community to take on grand challenges, understand emerging trends, identify the critical inflections, and drive our sustainability, Environment, Social, and Governance (ESG) practices that will define the next generation of semiconductors and continued impact.

The Impact You’ll Make

The successful candidate will learn, collaborate, and work with Lam’s expert Data Scientists to develop deep and advanced next gen Probabilistic Models and Bayesian optimization. Specifically, the intern will be involved in the design and development of surrogate models, custom acquisition functions, active learning strategies, etc. The candidate will have hands-on experience in both open-source and Lam’s proprietary ML Libraries and tools, and will work closely with Lam’s Data Science team in Singapore.

What You’ll Do

We are looking for an intern who wishes to enhance his / her skills in Machine Learning (ML) / Deep Learning (DL) Models with the focus in Probabilistic modeling, surrogate models, and Bayesian Optimization. Lam’s expert Data Science Team has recently shown that Bayesian Optimization can significantly help process development. This internship will build on top of this groundbreaking work that made use of Bayesian optimization (BO) techniques to enhance the efficiency of semiconductor process development. Process engineers use their expertise to solve these problems through extensive experimentation, which is cost intensive and requires a lot of time. Bayesian Optimization offers a powerful framework for addressing these challenges by iteratively selecting suitable experimental conditions. Your responsibilities include : 

• Contribute to the development on BO based framework for modeling and optimization.
• Explore and implement different probabilistic surrogate models such as Gaussian Processes (and variants) to represent process behavior.
• Implement and test specific features to help improve the BO framework.
• Validate the overall framework by carrying out experiments on virtual datasets.

Who We’re Looking For

• Post-Graduates (Final Year Students - Master / PhD Program) with strong passion and understanding of Statistical Machine Learning, Probabilistic Modeling, and Bayesian optimization.
• Good knowledge and understanding in statistical machine learning, probabilistic modeling, and Bayesian Optimization.
• Strong proficiency in Python programming and debugging, Model development and validation, associated libraries like PyTorch, BoTorch, NumPy, Pandas, etc.
• Self-driven personality with the team-work mindset and self-reliant working style.
• Previous relevant works in Bayesian Optimization or related fields.
• This is purely a data science / ML work and hence, NO knowledge on semiconductor, hardware, etc. is required.

Preferred Qualifications

Our Commitment

We believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results.

Lam Research ("Lam" or the "Company") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees.

Lam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories – On-site Flex and Virtual Flex. ‘On-site Flex’ you’ll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. ‘Virtual Flex’ you’ll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.