Applied Materials · 1 day ago
Director - AI and Advanced Analytics
Santa Clara,CA
Full-time
Onsite
Director/Executive
$220K/yr - $302K/yr
6+ years expApplied Materials is a global leader in materials engineering solutions, and they are seeking a Director-level Scientific Machine Learning leader to drive the strategy, development, and deployment of next-generation ML systems. This role involves leading initiatives that translate scientific ML research into production-grade platforms, while mentoring high-performing teams of ML engineers and scientists.
ElectronicsManufacturingSemiconductorSoftware
Responsibilities
Define and execute the Scientific ML roadmap across:
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:
Drive research-to-production translation, ensuring models are:
Establish robust evaluation frameworks:
Lead development of:
Lead development of:
Set standards for interpretability and scientific explainability:
Define data collection/annotation strategy spanning:
Ensure production Scientific ML systems meet reliability and governance expectations:
Partner with platform teams to improve tooling:
Deliver measurable impact such as:
Qualification
Required
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
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)
Benefits
Comprehensive benefits package
Participation in a bonus and a stock award program
Company
Applied Materials
Applied Materials is a semiconductor and display equipment company that offers materials engineering solutions.
10001+ employees
H1B Sponsorship
Applied Materials has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (435)
2024 (465)
2023 (362)
2022 (429)
2021 (456)
2020 (354)
Funding
Current Stage
Public CompanyTotal Funding
$2.1BKey Investors
Stonnington GroupUS Department of Energy
2025-02-24Post Ipo Debt· $2B
2023-06-27Post Ipo Equity· $0.38M
2022-10-19Grant· $100M
Leadership Team
Omkaram Nalamasu
CTO and Senior VP of Applied Materials; President of Applied Ventures
Tony Chiang, Ph.D
VP, CTO Applied AI
Recent News
2026-01-23
2026-01-23
2026-01-22
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