Data Science AI​/ML Lead

Overview

The AI Hub within Global Information Systems (GIS) 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. 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.

This role works closely with subject matter experts, data engineers, ML engineers, and product managers to ensure AI solutions are robust, explainable, and aligned to business needs.

• 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 (RAG vs. fine-tuning decision-making).
• Guide the use of out-of-the-box foundation models and platforms, while leading development of custom solutions when needed (e.g., vision models, domain specific fine-tuning).
• Partner with business stakeholders and SMEs to identify high value opportunities and craft well-formed data science problem statements with strong ROI potential.

• Perform advanced data analysis using statistical and scientific methods; build proof of concept models that scale to production deployments.
• Mine and analyze large-scale data sets 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 and model serving.

• 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 an 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.

• Strong in presenting data and analysis in a visually intuitive way to a broad set of stakeholders (technical and non-technical), experience with viz tools based on Python (Dash, etc.).
• 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 systems, including RAG pipelines, embedding models, vector databases, and efficient inference techniques.
• Feature pipelines and/or model development experience in Vision, data augmentation/automated labeling, or time-series or reinforcement learning (OpenAI Gym, PyTorch, Tensor Flow, Keras, scikit-learn, etc.).
• Strong programming experience in Python with demonstrated experience in package development (or open-source projects, hackathons, etc.), API development.
• Strong in data/feature engineering with Pandas/PySpark, etc.
• MS/PhD in engineering disciplines preferred

Prior experience in any of the following areas would be great!

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