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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Algorithm Developer - **Company:** Applied Materials - **Location:** Santa Clara, CA, United States (Remote available) - **Salary:** $161,000.0 - $221,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Algorithm Design, Artificial Neural Networks, Unit Testing, Profiling, Code Review, Nvidia CUDA, Computer Programming, Continuous Integration, Data Structures, Software Debugging, Software Design Patterns, Desktop Publishing, Distributed Computing Environment, Experimental Data, Python (Programming Language), Machine Learning, OpenMP, Performance Tuning, Tensorflow, Software Construction, Software Engineering, Pytorch, Deep Learning, Parallel Computation, Gpu Programming, Gaussian, Pytest, Git Flow, Integration Tests, Information Technology, Optimization Algorithms, Data Analytics, Codebase, Code Restructuring, Software Version Control - **Published:** July 16, 2026 - **Apply:** https://dejobs.org/x/x/1D7B05AAF508406A87368F5E93657599/job/ ## About the Role * Computer Science Foundation: Strong understanding of algorithms, data structures, computational complexity * Software Engineering: Clean code practices, design patterns, unit testing, modular architecture * Programming: Expert-level Python * Deep 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 strategies * Critical Thinking: Ability to reason through algorithmic choices, customize for problem constraints, debug systematically Education & Experience * MS or PhD in Computer Science, Applied Mathematics, Electrical Engineering, or related field * Computer Science degree strongly preferred * Relevant coursework: Algorithms, Machine Learning, Optimization, Software Engineering Preferred: * GPU programming (CUDA, performance optimization) * Parallel computing (MPI, OpenMP, distributed training) * Bayesian methods (Gaussian processes, uncertainty quantification) * Active learning and sample-efficient optimization Software Engineering * Experience refactoring legacy code or working with large codebases * CI/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 optimization Domain & Research * Publications in ML conferences/journals * Understanding of semiconductor manufacturing or materials science * Experience with experimental design * Knowledge of statistical inference from noisy experimental data * Experience with sparse, noisy, high-dimensional data * PyTorch/TensorFlow internals knowledge ## Description 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., Algorithm Development * Design and implement deep learning models for semiconductor process optimization (recipe inputs * metrology outputs) * Develop Bayesian optimization strategies for sample-efficient experimental design with expensive experiments Software Engineering * Write clean, maintainable, scalable code following software engineering best practices * Apply design patterns to algorithm implementations * Develop comprehensive unit tests and validation frameworks for algorithms * Refactor prototype algorithms into production-quality code integrated with AppliedPRO architecture * Conduct and participate in code reviews, fostering team code quality standards * Document design decisions, trade-offs, and algorithmic approaches clearly * Build surrogate models and active learning frameworks for sparse, noisy manufacturing data * Create novel algorithms that combine data-driven approaches with domain constraints * Implement algorithms with proper data structures, computational complexity awareness, and performance optimization Problem Solving & Innovation * Translate semiconductor manufacturing challenges into well-defined ML problems * Reason through trade-offs between accuracy, speed, and maintainability * Customize algorithms to handle sparse data, noisy measurements, and expensive experiments * Debug systematically when algorithms underperform (not trial-and-error) * Propose and implement innovative solutions to complex optimization problems Collaboration * Work with domain experts to understand semiconductor process constraints * Communicate complex algorithmic concepts to non-technical stakeholders * Collaborate with team members on algorithm design and code architecture * Contribute to team knowledge sharing on ML techniques and software best practices ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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