Manager, Data Scientist

Applied Materials•Austin, TX

12d•Onsite

About The Position

Who We Are Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. What We Offer Salary: $176,000.00 - $242,000.00 Location: Austin,TX You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .

Requirements

Bachelors in one of the following Computer Science, Artificial Intelligence or Data Science
Recognized technical expert in Generative AI, Agentic AI, Multi-Agent Architectures, and Enterprise AI Platforms .
Deep expertise in Large Language Models (LLMs), RAG, vector databases, knowledge graphs, AI orchestration frameworks, machine learning, and cloud-native architectures.
Strong hands-on software engineering capabilities with Python and modern AI development frameworks.
Demonstrated ability to design scalable, production-ready AI systems across multiple technology domains.
Anticipates emerging AI technology trends and identifies opportunities to create competitive advantages through AI-driven automation and intelligence.
Partners with business leaders to define AI strategy, prioritize use cases, and deliver measurable business outcomes through autonomous and intelligent systems.
Understands manufacturing, supply chain, engineering, operational, and enterprise business processes and how Agentic AI can transform them.
Leads complex AI transformation initiatives from strategy through implementation and production deployment.
Drives cross-functional teams delivering enterprise-scale Agentic AI and automation solutions.
Influences technical direction, architecture standards, and AI governance across the organization.
Solves highly complex and ambiguous business and technical problems through innovative application of AI, machine learning, and autonomous agent technologies.
Develops novel approaches for reasoning, planning, orchestration, workflow automation, and knowledge-driven decision making.
Balances experimentation and innovation with production-grade engineering principles.
Influences enterprise AI strategy, technology investments, architecture decisions, and adoption of next-generation AI capabilities.
Delivers scalable AI solutions that improve productivity, operational performance, decision quality, and business agility.
Shapes long-term AI platform roadmaps and standards for the organization.
Communicates complex AI concepts and architectures effectively to executive leadership, technical teams, and business stakeholders.
Drives alignment across diverse organizations and builds consensus around AI strategy and solution approaches.
Effectively mentors teams and promotes adoption of AI best practices across the enterprise.

Nice To Haves

7+ years of software engineering, data science, machine learning, or AI experience.
3+ years building production-grade AI/ML solutions using Python.
3+ years developing Generative AI, Agentic AI, Multi-Agent Systems, RAG, Knowledge Graphs, or LLM applications.
Experience with Azure AI, Databricks, OpenAI, LangGraph, Semantic Kernel, CrewAI, AutoGen, vector databases, and cloud-native architectures.
Demonstrated track record delivering enterprise-scale AI products from concept through production.

Responsibilities

Lead the architecture, design, and implementation of Agentic AI solutions and Multi-Agent Systems that solve complex business and manufacturing challenges through autonomous reasoning, planning, orchestration, and execution.
Drive the development of AI agents using modern frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Agents, Azure AI Foundry) to enable decision intelligence, workflow automation, knowledge retrieval, and operational optimization.
Serve as a hands-on technical leader responsible for building scalable AI platforms, including agent orchestration, memory management, tool integration, Retrieval-Augmented Generation (RAG), knowledge graphs, ontologies, and enterprise AI architectures.
Lead the development of advanced AI capabilities, including reasoning agents, planning agents, orchestration agents, code-generation agents, analytics agents, and domain-specific copilots that improve business outcomes and operational efficiency.
Collaborate with business stakeholders, product teams, engineers, data scientists, and subject matter experts to identify high-value AI use cases and translate them into production-grade AI solutions.
Establish AI engineering best practices covering LLMOps, AI governance, evaluation frameworks, observability, security, safety, prompt engineering, context engineering, model optimization, and continuous improvement.
Architect and develop enterprise AI platforms leveraging Azure AI, Databricks, Python, vector databases, graph databases, cloud-native technologies, and modern machine learning frameworks.
Build and optimize agent memory architectures, semantic layers, knowledge repositories, and enterprise ontologies to improve reasoning quality, contextual awareness, and autonomous execution.
Lead proof-of-concept development, rapid prototyping, and production deployments while ensuring scalability, reliability, maintainability, and measurable business value.
Mentor and guide AI engineers and data scientists while remaining actively involved in coding, architecture reviews, solution design, model development, and technical problem solving.
Stay current with emerging advances in Generative AI, Agentic AI, foundation models, reasoning systems, and autonomous agents, driving adoption of innovative technologies across the organization.