AI Algorithm DeveloperApplied 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.We are seeking an AI Algorithm Developer to design and implement machine learning algorithms for semiconductor manufacturing process optimization. This role requires a strong foundation in computer science fundamentals, software engineering best practices, and deep learning/optimization algorithms. You will work on challenging problems involving sparse, noisy, high-dimensional data from semiconductor equipment, building models that predict on-wafer performance from recipe parameters.The ideal candidate combines algorithmic depth (can reason through "why", not just implement), clean code practices (design patterns, testing, maintainable systems), and critical thinking (customizes algorithms to problem constraints rather than applying cookbook solutions).Algorithm DevelopmentDesign and implement deep learning models for semiconductor process optimization (recipe inputs ?
metrology outputs)Develop Bayesian optimization strategies for sample-efficient experimental design with expensive experimentsSoftware EngineeringWrite clean, maintainable, scalable code following software engineering best practicesApply design patterns to algorithm implementationsDevelop comprehensive unit tests and validation frameworks for algorithmsRefactor prototype algorithms into production-quality code integrated with AppliedPRO architectureConduct and participate in code reviews, fostering team code quality standardsDocument design decisions, trade-offs, and algorithmic approaches clearlyBuild surrogate models and active learning frameworks for sparse, noisy manufacturing dataCreate novel algorithms that combine data-driven approaches with domain constraintsImplement algorithms with proper data structures, computational complexity awareness, and performance optimizationProblem Solving & InnovationTranslate semiconductor manufacturing challenges into well-defined ML problemsReason through trade-offs between accuracy, speed, and maintainabilityCustomize algorithms to handle sparse data, noisy measurements, and expensive experimentsDebug systematically when algorithms underperform (not trial-and-error)Propose and implement innovative solutions to complex optimization problemsCollaborationWork with domain experts to understand semiconductor process constraintsCommunicate complex algorithmic concepts to non-technical stakeholdersCollaborate with team members on algorithm design and code architectureContribute to team knowledge sharing on ML techniques and software best practicesKey RequirementsComputer Science Foundation: Strong understanding of algorithms, data structures, computational complexitySoftware Engineering: Clean code practices, design patterns, unit testing, modular architectureProgramming: Expert-level PythonDeep Learning: Neural network architectures, training dynamics, optimization techniques (can explain "why", not just use libraries)Optimization Algorithms: Experience with gradient-based methods, Bayesian optimization, or evolutionary strategiesCritical Thinking: Ability to reason through algorithmic choices, customize for problem constraints, debug systematicallyEducation & ExperienceMS or PhD in Computer Science, Applied Mathematics, Electrical Engineering, or related fieldComputer Science degree strongly preferredRelevant coursework: Algorithms, Machine Learning, Optimization, Software EngineeringPreferred:GPU programming (CUDA, performance optimization)Parallel computing (MPI, OpenMP, distributed training)Bayesian methods (Gaussian processes, uncertainty quantification)Active learning and sample-efficient optimizationExperience refactoring legacy code or working with large codebasesCI/CD, testing frameworks (pytest, unittest, integration testing)Design patterns in practice (Factory, Observer, Strategy, etc.)Version control best practices (Git workflows, code reviews)Performance profiling and optimizationPublications in ML conferences/journalsUnderstanding of semiconductor manufacturing or materials scienceExperience with experimental designKnowledge of statistical inference from noisy experimental dataExperience with sparse, noisy, high-dimensional dataPyTorch/TensorFlow internals knowledgeAdditional InformationTime Type: Full timeEmployee Type: New College GradTravel: Yes, 10% of the TimeRelocation Eligible: NoThe 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.For 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.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.In addition, Applied endeavors to make our careers site accessible to all users.
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