Director - AI and Advanced Analytics
Applied Materials, Inc., Santa Clara, California, us, 95053
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**.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-end—from **problem framing and data strategy** to **deployment, evaluation, and scientific validation**.* 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* 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.- 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**).* 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).* 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.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**.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-end—from **problem framing and data strategy** to **deployment, evaluation, and scientific validation**.* 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* 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.- 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**).* 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 #J-18808-Ljbffr
+ Transformers, generative models, self-/semi-supervised learning
+ Strong intuition for generalization, inductive bias, and model failure modes* 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.- 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**).* 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).* 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.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**.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-end—from **problem framing and data strategy** to **deployment, evaluation, and scientific validation**.* 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* 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.- 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**).* 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 #J-18808-Ljbffr