Director - AI and Advanced Analytics

Applied Materials•Santa Clara, CA

About The Position

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 Monitoring, alerting, rollback, privacy/security, documentation Reproducibility standards (experiment tracking, data versioning, model cards) 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. Monitoring, alerting, rollback, privacy/security, documentation Reproducibility standards (experiment tracking, data versioning, model cards) 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. 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 Establish robust evaluation frameworks: Ensure out-of-distribution (OOD) robustness and clear model failure modes Deliver measurable impact such as: Improved prediction quality: better generalization to new chemistries/process conditions; quantified uncertainty and improved decision confidence

Requirements

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
Physics-informed neural networks (PINNs)
PDE-constrained learning, differentiable physics, operator learning
Surrogate modeling for scientific simulation
Strong proficiency in Python and modern ML frameworks (PyTorch preferred).
Ability to communicate complex technical decisions to non-technical stakeholders and drive alignment across R&D and engineering.
Publications, patents, open-source contributions in scientific ML/materials AI.
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.

Nice To Haves

Molecular/materials GNNs, Graph Transformers, equivariant models preferred
Strong grounding in math/stats: Linear algebra, optimization, probability, scientific experimentation / uncertainty
Domain depth in one or more: Materials science (batteries, catalysts, polymers, semiconductors, alloys)
Computational chemistry/physics (DFT, MD), continuum modeling (CFD/FEA), multiphysics simulation
Uncertainty quantification (UQ), calibration, Bayesian methods
Active learning, Bayesian optimization, multi-objective optimization
Scientific data systems: ELN/LIMS integration, instrument pipelines, data provenance
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).

Responsibilities

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.
Monitoring, alerting, rollback, privacy/security, documentation
Reproducibility standards (experiment tracking, data versioning, model cards)
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.
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
Establish robust evaluation frameworks: Ensure out-of-distribution (OOD) robustness and clear model failure modes
Deliver measurable impact such as: Improved prediction quality: better generalization to new chemistries/process conditions; quantified uncertainty and improved decision confidence