CAE Engineer – AI CFD and Thermal

Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. 

What We Offer

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You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more. 

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Job Description

• Senior Engineer / Lead Engineer – AI CFD will Drive AI innovation in CFD/Thermal domain
• 5+ years’ experience in CFD/Thermal simulations related to Semiconductor/Aero/Automotive Product Development / Manufacturing Engineering
• 2+ years’ experience in implementing AI solutions in CFD/Thermal simulations
• Requires strong knowledge of CFD, Heat Transfer and Pre & Post processing of CFD Tasks
• Must have knowledge of commercially available CFD analysis tools such as Ansys Mesher, Ansys Fluent, Ansys WB, CFD-ACE+ modules
• Knowledge of statistical methods and tools (JMP, Minitab) and AI tools (Ansys SimAI and Altair PhysicsAI)
• Exposure to Chemical reactions/Plasma simulations is an added advantage

Key Responsibilities

• Collaborate with stakeholders to understand business challenges in the CFD/Thermal space and solve them using API based customization and AI methodologies
• Automating mesh generation, solver setup, and post-processing of CFD and thermal analysis results
• Building optimization pipelines for thermal and fluid systems using AI-assisted approaches
• Evaluate, validate, and benchmark model performance using appropriate metrics
• Deploying AI models into production environments in collaboration with IT/AI teams
• Establish monitoring and maintenance processes to ensure model accuracy over time
• Ensure that all AI solutions comply with organizational data security, confidentiality, and regulatory requirements
• Document workflows, results, and lessons learned for organizational knowledge sharing
• Stay updated on advancements in neural networks, multi-physics simulations, surrogate modelling and physics-informed learning techniques

Functional Knowledge

• Must have in-depth knowledge of Fluid dynamics and Heat Transfer
• Strong skills in problem solving, teamwork and quality
• Strong programming skills in Python and C++ for automation and solver integration
• Familiarity with Bayesian optimization and DOE
• Experience with ML frameworks like TensorFlow
• Knowledge of surrogate modeling, reduced-order modeling (ROMs), and regression techniques
• Experience in data handling (large-scale CFD datasets) and feature engineering (feature extraction from flow fields like velocity, pressure, turbulence quantities)
• Experience in domain-specific AI use cases (Semiconductor, manufacturing etc.)

Academic Qualifications

• B.E/ B. Tech, MS/M.Tech in Mechanical - Thermal and Fluids Engineering or MS/M.Tech in Chemical engineering with 5+ years of experience in Flow/Thermal/Chemical Engineering domains.

Preferred Attributes

• Strong problem-solving and analytical mindset
• Strong attention to detail and organizational skills
• Excellent communication skills for presenting complex findings to stakeholders
• Ability to work independently and collaboratively in a fast-paced environment
• Proactive approach to process improvement and automation

Additional Information

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Relocation Eligible:

Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.