This role pays more than
86%
of similar roles. Most pay
$170,400–$236,325
— the shaded band above.
At the midpoint, this role pays about
$258k
versus about
$203k
for comparable roles.
Based on 240 similar postings.
Employer
About Lam Research
Lam Research Corporation is a leading American supplier of wafer-fabrication equipment and services to the global semiconductor industry.
Lam Research currently has
299 open roles
on FindRole.
Listed pay typically runs
$114,000–$253,000
across 141 roles with salary data.
As a Sr. Applied Intelligence Architect within the Enterprise AI team, you will establish how the company selects, adapts, evaluates, and governs various intelligence models to improve enterprise operations and product experiences. You will build model-agnostic interfaces, reusable contracts, and abstraction layers while designing evaluation systems for grounded correctness, safety, and cost efficiency. Your daily work involves developing agent workflows, fine-tuning via LoRA or distillation, and creating closed-loop learning systems that capture operational signals to improve models over time. You will utilize Python, modern machine learning frameworks, and tools like Azure AI Foundry, Kubernetes, and MLflow. The role focuses on solving the challenge of integrating advanced intelligence with enterprise knowledge, physics engines, and digital twins while ensuring secure, reproducible outcomes across complex engineering workflows and manufacturing-related product domains.
Own the enterprise AI architecture and implementation roadmap for various model types including frontier, open-weight, and specialized models.
Translate business requirements into technical solutions involving model routing, retrieval, agent workflows, and inference patterns.
Design model-agnostic interfaces and abstraction layers to ensure system modularity and prevent vendor lock-in.
Establish evaluation systems to measure performance metrics such as accuracy, safety, latency, cost, and business impact.
Create reusable engineering patterns for fine-tuning, quantization, distillation, and prompt engineering.
Architect closed-loop learning systems that capture operational signals to improve models and workflows over time.
Embed governance by design through secure data handling, auditability, and defined authority boundaries.
Convert successful experiments into reference architectures and production-ready assets for internal teams.
What we're looking for
Bachelor's degree with 12+ years experience, Master's with 8+ years, PhD with 5+ years, or equivalent practical experience.
Substantial experience in applied AI/ML systems, AI architecture, distributed systems, cloud engineering, or advanced software engineering.
Demonstrated success designing and deploying production AI systems using foundation models, multimodal models, retrieval, tools, and agents.
Deep understanding of the model lifecycle including evaluation, selection, fine-tuning, inference, observability, safety, and cost.
Strong hands-on technical capability with Python and modern AI or machine learning frameworks.
Experience creating scalable architectures across APIs, services, event-driven systems, data platforms, containers, and cloud infrastructure.
Ability to translate ambiguous business problems into measurable evaluations, technical decisions, and production roadmaps.
Experience with open-weight models, multi-model routing, agentic systems, or enterprise AI platforms (preferred); experience in semiconductor or manufacturing industries (plus).