AI Algorithm Developer

Applied Materials; Santa Clara (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Applied Materials in Santa Clara is seeking an AI Algorithm Developer to design and implement machine learning algorithms aimed at optimizing semiconductor manufacturing processes. This role calls for expertise in deep learning alongside a solid computer science foundation and best software engineering practices.

The ideal candidate should possess a keen understanding of algorithms, clean coding practices, and a critical approach to problem-solving in a collaborative environment with domain experts.

Qualifications

• Strong understanding of algorithms, data structures, and computational complexity.
• Expert-level Python programming.
• Deep learning expertise with neural network architecture.

Responsibilities

• Design and implement deep learning models for semiconductor process optimization.
• Write clean, maintainable, scalable code following software engineering best practices.
• Translate semiconductor manufacturing challenges into well-defined ML problems.

Skills

Expert-level Python programming

Deep learning expertise

Algorithm understanding

Critical thinking

Clean code practices

Education

MS or PhD in Computer Science, Applied Mathematics, Electrical Engineering, or related field

Job description

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. The ideal candidate combines algorithmic depth, clean code practices, and critical thinking.

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

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
• 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

Key Requirements

• Strong understanding of algorithms, data structures, and computational complexity
• Clean code practices, design patterns, unit testing, and modular architecture
• Expert‑level Python programming
• Deep learning expertise with neural network architecture, training dynamics, and optimization techniques
• Experience with gradient‑based methods, Bayesian optimization, or evolutionary strategies
• Critical thinking: ability to reason through algorithmic choices and 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 and 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 and statistical inference from noisy data
• Experience with sparse, noisy, high‑dimensional data
• PyTorch/TensorFlow internals knowledge

Additional Information

Time type: Full time. Employee type: New College Grad.

Travel: Yes, 10% of the time. Relocation eligible: No.

Salary offered will be based on multiple factors including location, hire grade, job‑related knowledge, skills, experience, and consideration of internal equity. Candidates may be eligible for bonus and stock award programs.

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. For accommodation requests, contact [e-mail] or call HR Direct Help Line at 877‑612‑7547, option 1.