Data Scientist - Agentic AI / ML

Applied Materials; Santa Clara (CA); On-site; USD 142,500 - 196,500; Full time

Job summary

Applied Materials is seeking a candidate for a role focused on machine learning in Santa Clara, CA. This position involves developing analytical models and enhancing business operations through GenAI and LLM projects. You will work collaboratively with cross-functional teams and have hands-on experience in implementing models using frameworks like PyTorch or TensorFlow.

The ideal candidate will possess an advanced degree in a quantitative field and 0-2 years of relevant industry experience.

Qualifications

• 0-2 years of industry experience in applied machine learning or related AI work.
• Hands-on experience with GenAI-focused applications and understanding of LLM architecture.
• MLOps including model deployment, versioning and performance monitoring in production environments.

Responsibilities

• Develop analytical models, algorithms and automated processes.
• Work on GenAI and LLM projects to enhance business operations.
• Collaborate with cross-functional teams to gather data for forecasting demand.

Skills

Machine Learning

Python

Collaboration

Data Mining

Communication Skills

Deep Learning Frameworks

SQL

Education

Advanced degree (MS or PhD) in a quantitative field

Tools

scikit-learn

Job description

Responsibilities

• Works in project teams developing difficult analytical models, algorithms and automated processes, research and develop machine learning models from inception to deployment.
• Work on GenAI and LLM projects, including fine‑tuning models, creating embedding‑based search systems, and developing AI‑driven prototypes to enhance business operations.
• Direct architects to design and solution GenAI architectures for stakeholders, specifically for plugin‑based solutions and custom GenAI application builds.
• Use data mining and machine learning algorithms to provide insights into historical and real‑time data for projects such as material forecast, demand forecast, pattern recognition, etc.
• Interfaces with stakeholders for requirements analysis and special requests and schedules; derives insights and works with business units to determine actions and KPI for those actions.
• Identify opportunities for forecast accuracy improvement and provide business insights and additional perspectives to Service Supply Chain leadership.
• Collaborate with Material Planning, Field Operations, Inventory Management, NPI, Production Demand Planning, Reliability Engineering, Service Campaigns Teams to gather data and bring information on key business drivers that impact the future demand on Service Parts and Accessories.

Qualifications

• Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research).
• 0-2 years of industry experience in applied machine learning or related AI work.
• Hands‑on experience building GenAI‑focused applications (e.g., agents, reasoning workflows, or RAG) and a solid understanding of how large language models are architected and operated.
• Can work collaboratively with cross‑functional teams.
• Hands‑on experience building GenAI‑focused application and applying LLMs and agentic AI (e.g., agents, reasoning workflows, or RAG).
• Have personally implemented models in common Deep Learning frameworks such as PyTorch, Jax or TensorFlow.
• MLOps, including model deployment, versioning and performance monitoring in production environments.
• Proficiency in Python, SQL, and tools such as scikit‑learn, and forecasting libraries.
• Excellent analytical and problem‑solving abilities, Machine Learning Concepts.
• Strong communication and storytelling skills — able to simplify complexity and influence executive stakeholders.

Location

Santa Clara, CA; on‑site full‑time.

Salary

$142,500.00 – $196,500.00

EEO Statement

Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.