Director AI and Advanced Analytics
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Job Location:
Santa Clara County, CA - USA
Monthly Salary:
$ 220000 - 302500
Posted on:
4 hours ago
Vacancies:
1 Vacancy
Job Summary
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:
$220000.00 - $302500.00Location:
Santa ClaraCAYoull 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 possiblewhile 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. Were 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.
We are seeking a Director-level Scientific Machine Learning leader to drive the strategy development and deployment of next-generation ML systems for physics- and chemistry-grounded applications including physics-informed neural networks (PINNs) operator learning graph representation learning for molecules/materials (GNNs equivariant GNNs Graph Transformers) and generative/inverse design.
This leader will own critical initiatives that translate cutting-edge scientific ML research into production-grade platforms and decision systems delivering measurable outcomes (e.g. accelerated materials discovery cycles reduced simulation cost improved experimental yield reduced time-to-formulation improved reliability of predictions) while building and mentoring high-performing teams of ML engineers research scientists and computational scientists.
The ideal candidate combines deep technical credibility in modern ML and strong grounding in physics/chemistry/materials science workflows with proven leadership delivering systems end-to-endfrom problem framing and data strategy to deployment evaluation and scientific validation.
Key Responsibilities
Strategy & Leadership
- 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.
Applied Research Production & Scientific Impact
- 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
Modeling & Scientific Systems
Lead development of:
Physics-informed & simulation-aware ML
- 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)
Materials discovery & molecular/crystal ML
- Graph neural networks for molecules crystals and periodic systems:
- Message passing Graph Transformers E(3)/SE(3)-equivariant GNNs
- Heterogeneous graphs linking compositionstructureprocessingproperty 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)
Optimization & closed-loop experimentation
- 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)
Interpretability & scientific trust
- Set standards for interpretability and scientific explainability:
- Feature attribution counterfactuals physics-consistency checks
- Uncertainty estimation and calibration as a first-class requirement
Data MLOps Reliability & Scientific Governance
- 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
Success Measures (Examples)
Deliver measurable impact such as:
- 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. 101000 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
Required Qualifications
- 612 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.
Preferred Qualifications
- 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).
Core Competencies
- 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.
Technology Stack (Typical)
- 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
Key Responsibilities
- 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.
Functional Knowledge
- Demonstrates broad and comprehensive understanding of different systems theories and practices
Business Expertise
- Applies broad industry and commercial awareness to drive financial and operational performance across business unit department or sub-functions
Leadership
- Leads through subordinate managers of managers; executes segment/functional business plans and contributes to the development of segment/functional strategy
Problem Solving
- Directs the resolution of highly complex or unusual business problems applying advanced analytical thought and judgment
Impact
- 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
Interpersonal Skills
- Negotiates and influences the opinions of others at the senior executive level and in external organizations; exercises sensitivity to the audience
We are seeking a Director-level Scientific Machine Learning leader to drive the strategy development and deployment of next-generation ML systems for physics- and chemistry-grounded applications including physics-informed neural networks (PINNs) operator learning graph representation learning for molecules/materials (GNNs equivariant GNNs Graph Transformers) and generative/inverse design.
This leader will own critical initiatives that translate cutting-edge scientific ML research into production-grade platforms and decision systems delivering measurable outcomes (e.g. accelerated materials discovery cycles reduced simulation cost improved experimental yield reduced time-to-formulation improved reliability of predictions) while building and mentoring high-performing teams of ML engineers research scientists and computational scientists.
The ideal candidate combines deep technical credibility in modern ML and strong grounding in physics/chemistry/materials science workflows with proven leadership delivering systems end-to-endfrom problem framing and data strategy to deployment evaluation and scientific validation.
Key Responsibilities
Strategy & Leadership
- 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.
Applied Research Production & Scientific Impact
- 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
Modeling & Scientific Systems
Lead development of:
Physics-informed & simulation-aware ML
- 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)
Materials discovery & molecular/crystal ML
- Graph neural networks for molecules crystals and periodic systems:
- Message passing Graph Transformers E(3)/SE(3)-equivariant GNNs
- Heterogeneous graphs linking compositionstructureprocessingproperty 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)
Optimization & closed-loop experimentation
- 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)
Interpretability & scientific trust
- Set standards for interpretability and scientific explainability:
- Feature attribution counterfactuals physics-consistency checks
- Uncertainty estimation and calibration as a first-class requirement
Data MLOps Reliability & Scientific Governance
- 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
Success Measures (Examples)
Deliver measurable impact such as:
- 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. 101000 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
Required Qualifications
- 612 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.
Preferred Qualifications
- 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).
Core Competencies
- 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.
Technology Stack (Typical)
- 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
Additional Information
Time Type:
Full timeEmployee Type:
Assignee / RegularTravel:
Yes 10% of the TimeRelocation Eligible:
NoThe salary offered to a selected candidate will be based on multiple factors including location hire grade job-related knowledge skills experience and with consideration of internal equity of our current team addition to a comprehensive benefits package candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program as applicable.
For all sales roles the posted salary range is the Target Total Cash (TTC) range for the role which is the sum of base salary and target bonus amount at 100% goal achievement.
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.
Required Experience:
Director
Key Skills
- Adobe Analytics
- Data Analytics
- SQL
- Attribution Modeling
- Power BI
- R
- Regression Analysis
- Data Visualization
- Tableau
- Data Mining
- SAS
- Analytics
About Company
Applied Materials, Inc. is the global leader in materials engineering solutions for the semiconductor, flat panel display and solar photovoltaic (PV) industries.
