AI Algorithm Developer

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

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

Time Type:

Full time

Employee Type:

Assignee / Regular

Travel:

Yes, 20% of the Time

Relocation Eligible:

Yes

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.