{"id":3528500,"url":"https://alion.io/job/applied-materials-aiml-engineer","title":"AI/ML Engineer","company":{"id":3903,"name":"Applied Materials","domain":"appliedmaterials.com","url":"https://alion.io/company/amat","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":87,"open_postings":294,"ghost_share":0.003,"stale_share":0.51,"repost_share":0.007,"time_to_fill_p50_days":52,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":184000,"max":253000,"currency":"USD","period":"year","gross":null,"usd_annual":253000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-28T00:00:00Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-31T23:59:40Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Who We Are\nApplied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology.\nWhat We Offer\nSalary:\n$184,000.00 - $253,000.00Location:\nSanta Clara,CAYou’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.\nAt 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.\nRole Overview\nJoin Applied Materials AI COE team to build intelligent systems that solve complex problems in semiconductor manufacturing.\nAs 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.\nYou’ll work with simulation environments, operational data, and real-world feedback to build intelligent systems that can learn, adapt, and improve complex factory operations.\nWhat You’ll Do\nDesign, train, evaluate, and deploy machine learning and deep learning models.\nDevelop reinforcement learning solutions for complex decision-making and optimization problems.\nBuild RL environments, reward functions, policies, and evaluation frameworks.\nTrain and optimize agents using simulation and operational data.\nExplore techniques including policy optimization, model-based RL, multi-agent systems, and hierarchical learning.\nBuild scalable training, inference, monitoring, and retraining workflows.\nPartner with software engineers to integrate AI capabilities into production and customer-facing products.\nContribute to MLOps, experiment tracking, model versioning, observability, and lifecycle management.\nResearch and prototype emerging technologies including Generative AI, Agentic AI, LLMs, RAG, and multi-agent systems.\nCollaborate with data scientists, software engineers, cloud architects, and semiconductor domain experts.\nWhat We’re Looking For\nRequired\nStrong foundation in machine learning and deep learning.\nHands-on experience building and deploying production ML systems.\nStrong Python development skills.\nExperience with PyTorch and/or TensorFlow.\nDemonstrated experience developing reinforcement learning solutions.\nStrong understanding of RL concepts including environments, policies, rewards, value functions, and exploration strategies.\nExperience with modern RL methods such as PPO, SAC, DQN, Actor-Critic, or similar approaches.\nExperience training and evaluating RL agents in simulation environments.\nAbility to formulate complex optimization problems as reinforcement learning problems.\nExperience with Git, testing, code reviews, CI/CD, and production software development.\n\nPreferred\nExperience building AI agents or autonomous decision-making systems.\nExperience with discrete-event simulation or simulation-driven learning.\nExperience combining RL with optimization techniques.\nKnowledge of scheduling, dispatching, operations research, or industrial optimization.\nExperience with Azure, AWS, or Google Cloud, Docker, Kubernetes, or MLflow.\nExperience with Generative AI, LLMs, agent frameworks, or RAG.\nSemiconductor manufacturing experience is a plus.\nWhy This Role\nReinforcement learning is a core strategic capability for this team, not simply a research topic. You’ll help develop next-generation manufacturing optimization solutions and intelligent agents capable of learning and adapting to complex factory operations.\nYou’ll join an experienced, collaborative team of data scientists, AI engineers, software developers, and cloud architects working on challenging industrial AI problems with direct customer and business impact.\nAdditional Information\nTime Type:\nFull timeEmployee Type:\nAssignee / RegularTravel:\nYes, 10% of the TimeRelocation Eligible:\nNoThe salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.\nFor all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.\nApplied 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.\nIn addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at ,or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. 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