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Mid-level role with generic title but niche optimization and semiconductor domain demands.
Core optimization and ML skills transfer, but semiconductor domain knowledge raises specificity.
Explicit 2–4 year requirement and specialized optimization/ML/domain skills create moderate strictness.
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Develop and implement optimization and planning algorithms to improve engineering workflows and automation quality.
Create and maintain benchmarking, visualization, and debugging tools to evaluate algorithm performance and engineering impact.
Collaborate with cross-functional teams to integrate applied ML and algorithmic solutions into production-grade software for engineering automation.
2-4 years of professional experience or M.Tech/MS graduates with relevant research/projects in applied ML, optimization, algorithms, or related fields.
Strong Python programming skills and good fundamentals in data structures and algorithms.
Experience or exposure to graph algorithms, pathfinding, optimization, computational geometry, numerical methods, or applied machine learning.
Ability to reason about constraints, edge cases, tradeoffs, and performance; curiosity to learn semiconductor test/validation workflows is required.
Candidates with demonstrated experience in planning problems, constraint handling, and evaluation metrics implementation in engineering workflows.
Experience working with practical machine learning experiments, reinforcement learning, or combinatorial optimization in applied settings.
Comfortable working with AI tools and collaborating with platform, AI, and domain engineers to build maintainable software solutions.