Algorithm Developer IV

Who We Are

Applied 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. 

What We Offer

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You’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. 

At 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. 

Position Overview

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).

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Key Responsibilities

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
• 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

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
• Conduct and participate in code reviews, fostering team code quality standards
• Document design decisions, trade-offs, and algorithmic approaches clearly

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

Required Qualifications

Technical Fundamentals (Must Have)

• 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

Technical Depth (Must Demonstrate)

• Can explain why algorithms work, not just what they do
• Understands trade-offs in algorithm design (accuracy vs. speed, exploration vs. exploitation, etc.)
• Can implement algorithms from scratch when needed (not just call libraries)
• Recognizes when to use appropriate data structures and explains complexity implications
• Writes code that others can read, maintain, and extend

Education

• 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

Experience

• 3+ years developing and deploying ML algorithms (post MS)
• 1+ years for PhD graduates with strong research contributions
• Demonstrated experience with production-quality code (not just research prototypes)
• Open-source contributions or well-documented code repositories (GitHub, etc.) preferred

Preferred Qualifications

Technical Skills

• GPU programming (CUDA, performance optimization)
• Parallel computing (MPI, OpenMP, distributed training)
• Bayesian methods (Gaussian processes, uncertainty quantification)
• Active learning and sample-efficient optimization
• Experience with sparse, noisy, high-dimensional data
• PyTorch/TensorFlow internals knowledge (not just usage)

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 (NeurIPS, ICML, ICLR, etc.)
• Understanding of semiconductor manufacturing or materials science
• Experience with experimental design (DOE, Latin hypercube sampling)
• Knowledge of statistical inference from noisy experimental data

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What We Value

• Deep understanding over surface-level knowledge
• Clean code that others can maintain
• Critical thinking and reasoning through trade-offs
• Intellectual curiosity and continuous learning
• Collaboration with domain experts
• Pragmatism - balance perfectionism with delivering solutions

Learning & Growth

• Work on challenging ML problems with real-world constraints (sparse data, expensive experiments)
• Exposure to semiconductor manufacturing domain
• Collaborate with world-class researchers and engineers
• Access to cutting-edge ML tools and infrastructure

Additional Information

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Travel:

Relocation Eligible:

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.