What You Will DoYour north star will be the guaranteed (empirical) validation of simulation systems.
In this role you will use and evaluate the cutting edge solutions developed by our Machine Learning and Solver teams. Ensure that our customers receive the highest value results by building a runtime evaluation mechanism. Develop a compelling data driven argument for this mechanism. Work with software engineers to implement your designs and demonstrate validity.
You will sit at the interface of teams of Physicists, AI researchers, Software Engineers and Computational Geometry experts. You are comfortable working with deep technical experts and bringing your own expertise to bear.
What We're Looking ForQualifications;
- Prior experience using or building physics simulators
- FEM, FEA, Molecular Dynamics, FDTD
- Experience as a systems engineer in a production environment
- working with Scientists and Engineers in a collaborative setting
- Basic understanding of solver mechanisms;
- Numerical Optimization, Convergence Criteria, Dampening approaches
- Working knowledge of ML basics
- back prop, loss functions, generators, embeddings, transformer models
- Understanding of statistics and data science methods
- Confidence intervals, uncertainty quantification, Bayes method
We are very excited to talk with you if you have
- Worked as a Systems Engineer for a production Software Solution in any of;
- Robotics, Chip Manufacturing, Aerospace
- Have leveraged simulation for design or data generation purposes.
- Have experience delivering solutions when needed
- Have worked on validation solutions for a production ML system
Engineering Expectations- Software engineering fundamentals
- Understanding of CI, regression testing, and validation discipline
- Excellent communication and documentation skills
- Comfortable running thousands of simulations and finding a needle in the haystack failure.
- Capable of defining an architecture with sufficient detail an Engineer could implement it with few open questions.
