Data Scientist AI​/ML Lead

The group you’ll be a part of

The Enterprise AI team within Office of CTO is a centralized, high impact group responsible for developing, scaling, and evangelizing AI capabilities across Lam Research. The team partners closely with product managers, engineering teams, and business units to deliver AI‑enabled solutions that drive measurable business value and accelerate Lam’s digital transformation.

We are seeking a highly skilled and versatile Data Science / AI / ML Lead to lead the development of advanced AI/ML solutions, including but not limited to statistical modeling, computer vision, LLM/RAG workflows, optimization, and domain‑specific modeling. This role works closely with product managers, forward‑deployed engineers, data engineers and AI/ML development engineers, translating high‑priority business challenges into robust, secure and explainable AI solutions aligned to business needs.

The ideal candidate combines deep technical expertise with strong stakeholder engagement skills, enabling them to act as a technical advisor, evangelist, and multiplier for AI capabilities across Lam.

• Develop, evaluate, and deploy state‑of‑the‑art ML/AI models including traditional ML, deep learning, computer vision, time‑series forecasting, and LLM‑based systems.
• Develop and maintain enterprise‑grade ontologies that model the complex domain of semiconductor engineering.
• Guide the use of OOTB foundation models and platforms, while leading development of custom solutions when needed (e.g., vision models, domain‑specific fine‑tuning).
• Evaluate and integrate emerging AI technologies, frameworks, and tools aligned with business requirements and a value‑driven mindset.

• Perform advanced data analysis using statistical and scientific methods; build proof‑of‑concept models that scale to production deployments. Mine and analyze large‑scale datasets to drive operational insights, optimization opportunities, and KPI improvements.
• Work with domain experts and ML engineers to develop feature stores, automated pipelines, and efficient MLOps workflows to speed up experimentation & model serving.
• Design and integrate ontology‑aware retrieval and reasoning (e.g., schema‑aware RAG, graph‑augmented retrieval, controlled vocabularies for prompts and tools) that grounds AI/ML systems (including LLMs, RAG pipelines, agents, and traditional ML) in consistent, machine‑interpretable business meaning.
• Establish measurable success criteria for ontology effectiveness (e.g., reduced ambiguity, improved retrieval accuracy, explainability, reuse across use cases).

• Work with platform teams to deploy scalable models using cloud infrastructure (e.g., Databricks, Azure ML, Azure Foundry, feature stores, model registries).
• Collaborate with software engineering teams to integrate models into applications and product workflows.
• Support internal communities of practice; mentor data scientists and engineers to propagate best practices.
• Act as a trusted technical advisor to business units on AI best practices, solution patterns, and technology selection.
• Develop and deliver training, demos, and internal enablement resources to uplift AI proficiency across Lam.

• Minimum of 15 years of related experience with a Bachelor’s degree; or 12 years and a Master’s degree; or a PhD with 8 years experience; or equivalent experience.
• Strong in presenting data and analysis in a visually intuitive way to a broad set of stakeholders (technical and non‑technical).
• Demonstrated breadth of understanding applicability of various ML/DL methods to various domains (e.g., time‑series, vision).
• Solid understanding of various ML and DL frameworks and in‑depth understanding of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real‑world advantages/drawbacks.
• Demonstrated expertise with Transformer architectures — including attention mechanisms, encoder–decoder designs, and fine‑tuning foundational models for NLP, CV, or multi‑modal tasks.
• Hands‑on experience building and optimizing Transformer‑based…