AI/ML Engineer
About The Position
Join Applied Materials’ AI COE team to build intelligent systems that solve complex problems in semiconductor manufacturing. The AI/ML Engineer designs, develops, and deploys production AI solutions across machine learning, deep learning, reinforcement learning, and AI agents. A key focus is using reinforcement learning to optimize dynamic manufacturing decisions such as scheduling, dispatching, planning, and resource allocation. The role uses simulation environments, operational data, and real-world feedback to build intelligent systems that learn, adapt, and improve factory operations. The engineer works with data scientists, software engineers, cloud architects, and semiconductor domain experts to develop manufacturing optimization solutions with direct customer and business impact. Reinforcement learning is a core strategic capability for the team, with emphasis on production-ready intelligent agents that can learn and adapt to complex factory operations.
Requirements
- Strong foundation in machine learning and deep learning.
- Hands-on experience building and deploying production ML systems.
- Strong Python development skills.
- Experience with PyTorch and/or TensorFlow.
- Demonstrated experience developing reinforcement learning solutions.
- Strong understanding of reinforcement learning concepts, including environments, policies, rewards, value functions, and exploration strategies.
- Experience with modern reinforcement learning methods such as PPO, SAC, DQN, Actor-Critic, or similar approaches.
- Experience training and evaluating reinforcement learning agents in simulation environments.
- Ability to formulate complex optimization problems as reinforcement learning problems.
- Experience with Git, testing, code reviews, CI/CD, and production software development.
Nice To Haves
- Experience building AI agents or autonomous decision-making systems.
- Experience with discrete-event simulation or simulation-driven learning.
- Experience combining reinforcement learning with optimization techniques.
- Knowledge of scheduling, dispatching, operations research, or industrial optimization.
- Experience with Azure, AWS, or Google Cloud, Docker, Kubernetes, or MLflow.
- Experience with Generative AI, LLMs, agent frameworks, or RAG.
- Semiconductor manufacturing experience.
Responsibilities
- Design, train, evaluate, and deploy machine learning and deep learning models.
- Develop reinforcement learning solutions for complex decision-making and optimization problems.
- Build reinforcement learning environments, reward functions, policies, and evaluation frameworks.
- Train and optimize agents using simulation and operational data.
- Explore policy optimization, model-based reinforcement learning, multi-agent systems, and hierarchical learning.
- Build scalable training, inference, monitoring, and retraining workflows.
- Partner with software engineers to integrate AI capabilities into production and customer-facing products.
- Contribute to MLOps, experiment tracking, model versioning, observability, and lifecycle management.
- Research and prototype emerging technologies, including Generative AI, Agentic AI, LLMs, RAG, and multi-agent systems.
- Collaborate with data scientists, software engineers, cloud architects, and semiconductor domain experts.
Benefits
- Comprehensive benefits package.
- Potential eligibility for participation in a bonus program.
- Potential eligibility for a stock award program.
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What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
