Director - AI and Advanced Analytics

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

Salary:
$220,000.00 - $302,500.00

Location:
Santa Clara,CA

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.

• Define and execute the Scientific ML roadmap across:
  • Physics-informed learning (PINNs, PDE-constrained learning, differentiable physics)
  • Operator learning (e.g., neural operators for PDE surrogates)
  • Graph learning for molecules, crystals, microstructures, and interaction networks (GNNs, equivariant models, Graph Transformers)
  • Generative & inverse design (diffusion models, VAEs, language-guided design, constrained generation)
• Lead and scale a team of applied ML researchers, computational scientists, and ML platform engineers; establish best practices and a culture of scientific rigor, reproducibility, and engineering excellence.
• Partner with R&D, experimental teams, simulation/HPC groups, product/engineering, and leadership to align AI investments with scientific priorities and operational constraints.
• Own resource planning, hiring, performance management, mentorship, and career development, building a high-output, high-quality org.

• Identify high-value opportunities where scientific ML creates step-change improvements, such as:
  • Accelerating materials discovery (candidate screening, property prediction, inverse design)
  • Reducing simulation cost via surrogate modeling and emulation (DFT/MD/CFD/FEA surrogates)
  • Improving experimental throughput using active learning, Bayesian optimization, and adaptive DoE
  • Improving reliability via uncertainty quantification (UQ), calibration, and robust validation
• Drive research-to-production translation, ensuring models are:
  • Scientifically valid, reproducible, interpretable where necessary
  • Maintainable, monitored, and fit for real-world decision-making
• Establish robust evaluation frameworks:
  • Guard against data leakage, dataset bias, spurious correlations
  • Ensure out-of-distribution (OOD) robustness and clear model failure modes
  • Define scientific validation criteria (agreement with known laws, conservation constraints, experimental confirmation)
• Lead development of:
  • Embedding systems and foundation representations for molecules/materials (self-supervised learning, contrastive learning)
  • Drift monitoring, retraining strategies, continual learning with evolving lab/simulation data

• PINNs / PDE-constrained training for forward/inverse problems, parameter estimation, and boundary-value problems
• Neural operators (operator learning) and multi-resolution surrogates for fast approximations of PDE solvers
• Differentiable programming workflows combining ML with simulators (where applicable)

• Graph neural networks for molecules, crystals, and periodic systems:
  • Message passing, Graph Transformers, E(3)/SE(3)-equivariant GNNs
  • Heterogeneous graphs linking composition-structure-processing-property data
• Multi-modal scientific representation learning:
  • Text (papers/ELN notes), structured data (composition/process), images (microstructure, SEM/TEM), spectra (XRD, Raman), and simulation outputs
• Generative & inverse design:
  • Diffusion/flow/energy-based models for structure generation and candidate proposal
  • Constraint-aware generation (stability, synthesizability, property targets)
  • LLM-assisted workflows for hypothesis generation, literature mining, and experiment planning (with guardrails)

• Bayesian optimization, active learning, and multi-armed bandits for:
  • Efficient candidate selection under budget constraints
  • Multi-objective optimization (e.g., performance vs. cost vs. stability)
• Reinforcement learning where appropriate for sequential decision processes (e.g., experimental scheduling, synthesis planning, control)

• Set standards for interpretability and scientific explainability:
  • Feature attribution, counterfactuals, physics-consistency checks
  • Uncertainty estimation and calibration as a first-class requirement

• Define data collection/annotation strategy spanning:
  • Simulation outputs (DFT/MD/CFD/FEA), experimental results, instrumentation pipelines
  • Standards for metadata, provenance, lineage, units, and versioning
• Ensure production Scientific ML systems meet reliability and governance expectations:
  • Monitoring, alerting, rollback, privacy/security, documentation
  • Reproducibility standards (experiment tracking, data versioning, model cards)
• Partner with platform teams to improve tooling:
  • Feature stores, vector databases (for retrieval + scientific context), model registry
  • Scalable training/inference pipelines, HPC integrations, workflow orchestration
  • Support for multi-fidelity datasets and distributed compute

• Discovery acceleration: reduced cycle time from hypothesis → validated candidate (e.g., weeks → days)
• Simulation acceleration: surrogate models reducing compute cost or wall-clock time (e.g., 10-1000× speedups in target regimes)
• Experimental efficiency: fewer experiments per breakthrough via active learning / Bayesian optimization
• Improved prediction quality: better generalization to new chemistries/process conditions; quantified uncertainty and improved decision confidence
• Platform outcomes:
  • Lower time-to-deploy new models, improved monitoring coverage, faster iteration cycles
  • Strong reproducibility and traceability (data/model versioning, audit trails)
• Org outcomes: strong hiring/retention, technical standards, mentorship, cross-functional credibility

• 6-12+ years building and deploying ML systems with demonstrated production and/or scientific impact (industry or research environments).
• 3+ years leading technical teams/projects (manager, tech lead, lead scientist, or equivalent), with a track record of developing senior talent.
• Deep expertise in modern ML and representation learning:
  • Transformers, generative models, self-/semi-supervised learning
  • Strong intuition for generalization, inductive bias, and model failure modes
• Demonstrated experience in Scientific ML, including one or more of:
  • Physics-informed neural networks (PINNs)
  • PDE-constrained learning, differentiable physics, operator learning
  • Surrogate modeling for scientific simulation
• Strong experience with graph ML for scientific domains:
  • Molecular/materials GNNs, Graph Transformers, equivariant models preferred
• Strong proficiency in Python and modern ML frameworks (PyTorch preferred).
• Strong grounding in math/stats:
  • Linear algebra, optimization, probability, scientific experimentation / uncertainty
• Ability to communicate complex technical decisions to non-technical stakeholders and drive alignment across R&D and engineering.

• Domain depth in one or more:
  • Materials science (batteries, catalysts, polymers, semiconductors, alloys)
  • Computational chemistry/physics (DFT, MD), continuum modeling (CFD/FEA), multiphysics simulation
• Experience with:
  • Uncertainty quantification (UQ), calibration, Bayesian methods
  • Active learning, Bayesian optimization, multi-objective optimization
  • Scientific data systems: ELN/LIMS integration, instrument pipelines, data provenance
• Publications, patents, open-source contributions in scientific ML/materials AI.
• Experience with large-scale compute and data:
  • HPC/GPU clusters, distributed training, Spark/Ray/Dask, workflow orchestration
• MS/PhD in Physics, Chemistry, Materials Science, CS, EE, Applied Math/Stats (or equivalent practical expertise).

• Scientific ML leadership: sets technical vision, makes pragmatic tradeoffs, delivers scientifically valid and useful systems.
• Scientific rigor: strong evaluation design, recognizes leakage, bias, drift, and OOD failure modes early.
• Product + R&D mindset: translates scientific goals into roadmaps with measurable outcomes and operational adoption.
• Team builder: scales talent while raising standards for reproducibility, quality, and engineering discipline.
• Cross-functional influence: aligns experimentalists, simulation experts, platform engineering, and leadership.

• Languages/Tools: Python, Jupyter, SQL, Linux/Unix, Git
• ML/DL: PyTorch, PyTorch Geometric, JAX (optional), Scikit-learn
• Scientific Computing: NumPy/SciPy, CUDA, HPC schedulers (SLURM), distributed training tooling
• Graph & Materials ML: equivariant GNN libraries (as applicable), graph transformers, molecular featurization toolkits
• GenAI: diffusion/flow models, LLM integration patterns, RAG + vector DB (for scientific context), safety/guardrails
• MLOps & Reproducibility: experiment tracking, data/model versioning, model registry, CI/CD, monitoring/drift detection
• Scientific Data: ELN/LIMS hooks, metadata/provenance standards, pipeline orchestration

• Serve as strategic interface with and across across business functions such as Sales, Operations, Engineering, Service and Finance for the purpose of BI application/platfrom and business alignment. Drive cross functional governance and alignment to leverage and optimize BI application and platform leverage, BKM sharing, standards, and delivery model.
• Directs organizational teams executing the build, test and deployment of complex, integrated BI application and platform solutions. Ensures these solutions are technically sound, cost effective and adhere to accepted industry best practices. Utilize data and metrics to drive continuous improvement.
• Develop and maintain relationships with BI and data management partners and suppliers. Drive assessment of vendor strategies, roadmaps and next generation technologies and incorporate into application and platform architectural strategy and capability roadmaps.
• Directs personnel providing BI, Big Data and AI/ML application and platform support services to meet customer performance, availability, service level agreemens and customer satisfaction targets. Ovresees monitoring of specific IT systems or set of systems and tuning of such systems for availability and performance. Drives completion of root cause analysis and resolution of outages or incident trends coordinating with infrastructure and technical teams, support providers and application vendors. Drives implemention of corrective and preventative actions. Responsible for executoin of lifecycle patches, point releases, and major upgrades.
• Plans and manages personnel to deliver BI, Big Data and AI/ML project and support service in area of responsibility within allocated budget. Develops project , service area and cost center budgets. Drive development of service area cost model optimization and implementation of optimizatoin initiatives. Ensure timely renewal of maintenance and subscription contracts.
• Monitors and manages staff to ensure adherence to GIS project management, software application development, testing, service management, change management, RCA and other relevant processes, standards, governance and controls. May manage execution of sox contols and testing, and support internal and external audits.
• Plan and manage large, highly complex cross functional Business intelligence, Big Data or AI/ML application or platform projects to ensure effective and efficient execution in line with guardrails of scope, timeline, budget and quality. Directs project managers managing medium to large scale projects.
• Manages personnel responsible for Business Intelligence, Big Data and AI/ML contingent worker strategic vendor relationships and delivery performance. Ensures contingent workforce utilization is optimized. Directs activities with strategic providers and GIS Vendor and Resource Management to identify gaps and opportunities and to recomend strategies for improvement.

• Demonstrates broad and comprehensive understanding of different systems, theories and practices

• Applies broad industry and commercial awareness to drive financial and operational performance across business unit, department or sub-functions

• Leads through subordinate managers of managers; executes segment/functional business plans and contributes to the development of segment/functional strategy

• Directs the resolution of highly complex or unusual business problems applying advanced analytical thought and judgment

• Guided by segment/functional strategy, impacts results of a department, business unit or sub-function or facilitates the work done by other segments/functions by providing support to impact the business

• Negotiates and influences the opinions of others at the senior executive level and in external organizations; exercises sensitivity to the audience

• Define and execute the Scientific ML roadmap across:
  • Physics-informed learning (PINNs, PDE-constrained learning, differentiable physics)
  • Operator learning (e.g., neural operators for PDE surrogates)
  • Graph learning for molecules, crystals, microstructures, and interaction networks (GNNs, equivariant models, Graph Transformers)
  • Generative & inverse design (diffusion models, VAEs, language-guided design, constrained generation)
• Lead and scale a team of applied ML researchers, computational scientists, and ML platform engineers; establish best practices and a culture of scientific rigor, reproducibility, and engineering excellence.
• Partner with R&D, experimental teams, simulation/HPC groups, product/engineering, and leadership to align AI investments with scientific priorities and operational constraints.
• Own resource planning, hiring, performance management, mentorship, and career development, building a high-output, high-quality org.

• Identify high-value opportunities where scientific ML creates step-change improvements, such as:
  • Accelerating materials discovery (candidate screening, property prediction, inverse design)
  • Reducing simulation cost via surrogate modeling and emulation (DFT/MD/CFD/FEA surrogates)
  • Improving experimental throughput using active learning, Bayesian optimization, and adaptive DoE
  • Improving reliability via uncertainty quantification (UQ), calibration, and robust validation
• Drive research-to-production translation, ensuring models are:
  • Scientifically valid, reproducible, interpretable where necessary
  • Maintainable, monitored, and fit for real-world decision-making
• Establish robust evaluation frameworks:
  • Guard against data leakage, dataset bias, spurious correlations
  • Ensure out-of-distribution (OOD) robustness and clear model failure modes
  • Define scientific validation criteria (agreement with known laws, conservation constraints, experimental confirmation)
• Lead development of:
  • Embedding systems and foundation representations for molecules/materials (self-supervised learning, contrastive learning)
  • Drift monitoring, retraining strategies, continual learning with evolving lab/simulation data

• PINNs / PDE-constrained training for forward/inverse problems, parameter estimation, and boundary-value problems
• Neural operators (operator learning) and multi-resolution surrogates for fast approximations of PDE solvers
• Differentiable programming workflows combining ML with simulators (where applicable)

• Graph neural networks for molecules, crystals, and periodic systems:
  • Message passing, Graph Transformers, E(3)/SE(3)-equivariant GNNs
  • Heterogeneous graphs linking composition-structure-processing-property data
• Multi-modal scientific representation learning:
  • Text (papers/ELN notes), structured data (composition/process), images (microstructure, SEM/TEM), spectra (XRD, Raman), and simulation outputs
• Generative & inverse design:
  • Diffusion/flow/energy-based models for structure generation and candidate proposal
  • Constraint-aware generation (stability, synthesizability, property targets)
  • LLM-assisted workflows for hypothesis generation, literature mining, and experiment planning (with guardrails)

• Bayesian optimization, active learning, and multi-armed bandits for:
  • Efficient candidate selection under budget constraints
  • Multi-objective optimization (e.g., performance vs. cost vs. stability)
• Reinforcement learning where appropriate for sequential decision processes (e.g., experimental scheduling, synthesis planning, control)

• Set standards for interpretability and scientific explainability:
  • Feature attribution, counterfactuals, physics-consistency checks
  • Uncertainty estimation and calibration as a first-class requirement

• Define data collection/annotation strategy spanning:
  • Simulation outputs (DFT/MD/CFD/FEA), experimental results, instrumentation pipelines
  • Standards for metadata, provenance, lineage, units, and versioning
• Ensure production Scientific ML systems meet reliability and governance expectations:
  • Monitoring, alerting, rollback, privacy/security, documentation
  • Reproducibility standards (experiment tracking, data versioning, model cards)
• Partner with platform teams to improve tooling:
  • Feature stores, vector databases (for retrieval + scientific context), model registry
  • Scalable training/inference pipelines, HPC integrations, workflow orchestration
  • Support for multi-fidelity datasets and distributed compute

• Discovery acceleration: reduced cycle time from hypothesis → validated candidate (e.g., weeks → days)
• Simulation acceleration: surrogate models reducing compute cost or wall-clock time (e.g., 10-1000× speedups in target regimes)
• Experimental efficiency: fewer experiments per breakthrough via active learning / Bayesian optimization
• Improved prediction quality: better generalization to new chemistries/process conditions; quantified uncertainty and improved decision confidence
• Platform outcomes:
  • Lower time-to-deploy new models, improved monitoring coverage, faster iteration cycles
  • Strong reproducibility and traceability (data/model versioning, audit trails)
• Org outcomes: strong hiring/retention, technical standards, mentorship, cross-functional credibility

• 6-12+ years building and deploying ML systems with demonstrated production and/or scientific impact (industry or research environments).
• 3+ years leading technical teams/projects (manager, tech lead, lead scientist, or equivalent), with a track record of developing senior talent.
• Deep expertise in modern ML and representation learning:
  • Transformers, generative models, self-/semi-supervised learning
  • Strong intuition for generalization, inductive bias, and model failure modes
• Demonstrated experience in Scientific ML, including one or more of:
  • Physics-informed neural networks (PINNs)
  • PDE-constrained learning, differentiable physics, operator learning
  • Surrogate modeling for scientific simulation
• Strong experience with graph ML for scientific domains:
  • Molecular/materials GNNs, Graph Transformers, equivariant models preferred
• Strong proficiency in Python and modern ML frameworks (PyTorch preferred).
• Strong grounding in math/stats:
  • Linear algebra, optimization, probability, scientific experimentation / uncertainty
• Ability to communicate complex technical decisions to non-technical stakeholders and drive alignment across R&D and engineering.

• Domain depth in one or more:
  • Materials science (batteries, catalysts, polymers, semiconductors, alloys)
  • Computational chemistry/physics (DFT, MD), continuum modeling (CFD/FEA), multiphysics simulation
• Experience with:
  • Uncertainty quantification (UQ), calibration, Bayesian methods
  • Active learning, Bayesian optimization, multi-objective optimization
  • Scientific data systems: ELN/LIMS integration, instrument pipelines, data provenance
• Publications, patents, open-source contributions in scientific ML/materials AI.
• Experience with large-scale compute and data:
  • HPC/GPU clusters, distributed training, Spark/Ray/Dask, workflow orchestration
• MS/PhD in Physics, Chemistry, Materials Science, CS, EE, Applied Math/Stats (or equivalent practical expertise).

• Scientific ML leadership: sets technical vision, makes pragmatic tradeoffs, delivers scientifically valid and useful systems.
• Scientific rigor: strong evaluation design, recognizes leakage, bias, drift, and OOD failure modes early.
• Product + R&D mindset: translates scientific goals into roadmaps with measurable outcomes and operational adoption.
• Team builder: scales talent while raising standards for reproducibility, quality, and engineering discipline.
• Cross-functional influence: aligns experimentalists, simulation experts, platform engineering, and leadership.

• Languages/Tools: Python, Jupyter, SQL, Linux/Unix, Git
• ML/DL: PyTorch, PyTorch Geometric, JAX (optional), Scikit-learn
• Scientific Computing: NumPy/SciPy, CUDA, HPC schedulers (SLURM), distributed training tooling
• Graph & Materials ML: equivariant GNN libraries (as applicable), graph transformers, molecular featurization toolkits
• GenAI: diffusion/flow models, LLM integration patterns, RAG + vector DB (for scientific context), safety/guardrails
• MLOps & Reproducibility: experiment tracking, data/model versioning, model registry, CI/CD, monitoring/drift detection
• Scientific Data: ELN/LIMS hooks, metadata/provenance standards, pipeline orchestration