Senior Research Specialist, AI & ML

Overview

Senior Research Specialist in AI and ML at KLA, working on intelligent agentic systems for semiconductor inspection and metrology. The role spans applied and fundamental research, from formulating problems to building prototypes and translating results into scalable solutions.

What you'll do

• Identify high-value opportunities for reinforcement learning, agentic AI, and multimodal reasoning in semiconductor inspection and metrology.
• Formulate scientific and engineering workflows as learning and sequential decision-making problems.
• Design intelligent agents that reason over multimodal data, use specialized tools and adapt their actions based on feedback.
• Develop and evaluate learning methods that improve agent reasoning, planning, tool use, and decision quality.
• Adapt foundation models to specialized scientific and industrial applications.
• Design rigorous experiments to assess performance, reliability, generalization, grounding, and failure recovery.
• Build reproducible prototypes and collaborate with engineering, infrastructure and domain teams to translate successful research into scalable solutions.
• Communicate research findings and collaborate with global multidisciplinary teams.
• Manage a team of engineers working in the space of AI.

What you'll need

• PhD in Computer Science, Electrical Engineering, Artificial Intelligence, Machine Learning or a related discipline, with 3 to 5 years of relevant research or industrial experience; or a master's degree with 5 to 10 years of relevant experience. Candidates with less experience may be considered if they demonstrate an exceptional research or engineering record.
• Strong foundation in at least one of the following: reinforcement learning; sequential decision-making; learning-based agents; foundation-model post-training; or planning and decision-making under uncertainty.
• Hands-on experience designing, implementing or evaluating intelligent agents involving planning, tool use, retrieval, memory, self-correction, multi-step execution, or learning from feedback.
• Working experience with LLMs, VLMs, multimodal foundation models, or generative foundation models.
• Sound understanding of reinforcement-learning concepts, including policies, rewards, value functions, exploration, credit assignment, policy optimization and evaluation.
• Ability to formulate real-world problems in terms of observations, states, actions, rewards, and sequential objectives.
• Strong foundation in deep learning, transformer architectures and representation learning.
• Hands-on experience developing, training, fine-tuning and evaluating models using PyTorch, JAX, TensorFlow or a comparable framework.
• Strong Python programming skills and the ability to develop maintainable and reproducible research software.
• Ability to formulate research and engineering problems mathematically.
• Experience designing controlled experiments, defining appropriate baselines, and selecting meaningful evaluation metrics.
• Ability to analyze experimental results, identify failure modes, and draw evidence-based conclusions.
• Ability to follow rapidly evolving research and translate relevant ideas into working prototypes.
• Strong written and verbal communication skills, with the ability to work effectively in multidisciplinary and geographically distributed teams.

Nice to have

• Research or industry experience in reinforcement learning, agentic AI, multimodal learning, or foundation-model adaptation.
• Experience developing intelligent systems that learn from feedback, interact with tools, and operate over extended decision-making horizons.
• Experience training, fine-tuning, or evaluating language, vision-language, or multimodal foundation models.
• Experience developing robust evaluation frameworks for agent performance, reliability, and generalization.
• Experience applying machine learning to complex scientific, engineering, or industrial problems.
• Evidence of strong technical depth across modern machine learning, deep learning, and AI systems research.
• Evidence of team leadership in small or large organizations.

Details

• Location: Chennai, India.