NeuralConcept
ML for CAX Application Engineer - US
About this role
As an ML for CAX Application Engineer, you will enhance product design workflows for engineering clients by implementing machine learning solutions and training teams on the AI platform. This role involves collaboration with clients to create tailored solutions and contributing to product development through customer feedback.
What you'll do
- Collaborate with client engineering teams to implement projects and solutions.
- Analyze data and apply deep-learning tools to engineering problems.
- Demonstrate value through proofs-of-concept in CAD and CAE.
- Train customers on effectively using the NC platform.
- Work with product team to integrate customer input into enhancements.
What they're looking for
- Expertise in Engineering and Machine Learning
- Proficient in Python programming
- Experience with machine learning libraries
- Strong simulation experience (CAD/CAE)
- Excellent communication skills
- Ability to convey technical content to diverse audiences
Benefits
- Competitive salary and growth opportunities
- Collaborative and multicultural work environment
- Professional development with supportive colleagues
- Flexible hybrid working model
- Comprehensive healthcare package and 401K matching
- Visible recognition for contributions
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NeuralConcept
NeuralConcept builds AI-powered solutions for engineering design and simulation workflows, serving automotive, aerospace, and energy sectors. The company is hiring Solutions Engineers and ML Application Engineers to implement machine learning capabilities, enhance product design workflows, and collaborate with engineering clients on tailored AI solutions.
- Website
- neuralconcept.com
Likely interview questions
- Can you walk us through a real-world engineering problem you solved using machine learning or deep learning? What was the domain (CFD, FEM, etc.) and what frameworks did you use?
- Describe your experience with CAD/CAE tools and simulation software. How have you integrated ML models with traditional engineering workflows?