> Markdown version of [/jobs/ext/4006182-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/4006182-ai-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** Applied Materials - **Location:** Santa Clara, CA, United States (Remote available) - **Salary:** $184,000.0 - $253,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Code Review, Continuous Integration, Discrete Event Simulation, Python (Programming Language), Machine Learning, Operational Data Store, Tensorflow, Reinforcement Learning, Google Cloud, Pytorch, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Deep Learning, Generative AI, Agentic-AI, Git, Kubernetes, Optimization Algorithms, Machine Learning Operations, Software Version Control, Docker - **Published:** October 29, 2026 - **Apply:** https://amat.wd1.myworkdayjobs.com/External/job/Santa-ClaraCA/AI-ML-Engineer_R2631042 ## About the Role * 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 RL concepts including environments, policies, rewards, value functions, and exploration strategies. * Experience with modern RL methods such as PPO, SAC, DQN, Actor-Critic, or similar approaches. * Experience training and evaluating RL 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. Preferred * Experience building AI agents or autonomous decision-making systems. * Experience with discrete-event simulation or simulation-driven learning. * Experience combining RL 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 is a plus. ## Description Join Applied Materials AI COE team to build intelligent systems that solve complex problems in semiconductor manufacturing. As an AI / Machine Learning Engineer, you'll design, develop, and deploy production AI solutions across machine learning, deep learning, reinforcement learning, and AI agents. A key focus of this role is using reinforcement learning (RL) to optimize dynamic manufacturing decisions such as scheduling, dispatching, planning, and resource allocation. You'll work with simulation environments, operational data, and real-world feedback to build intelligent systems that can learn, adapt, and improve complex factory operations. What You'll Do * Design, train, evaluate, and deploy machine learning and deep learning models. * Develop reinforcement learning solutions for complex decision-making and optimization problems. * Build RL environments, reward functions, policies, and evaluation frameworks. * Train and optimize agents using simulation and operational data. * Explore techniques including policy optimization, model-based RL, 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.