Computational Engineering Expert

Responsibilities

• Leverage expertise in computational, simulation, or systems engineering to inform advanced AI benchmarking and evaluation processes.
• Analyze and provide feedback on AI models’ performance in realistic engineering scenarios related to CFD, FEA, robotics, and more.
• Review, assess, and enhance technical datasets, workflows, and simulation results for quality and accuracy.
• Utilize domain-specific tools such as ANSYS, Abaqus, COMSOL, OpenFOAM, and MATLAB to validate engineering solutions.
• Create detailed written assessments and communicate complex technical insights clearly with the team.
• Collaborate with a diverse group of domain experts to identify gaps and opportunities for AI model improvement.
• Contribute to the development of robust, real-world benchmarks for AI systems in computational engineering fields.

Requirements

• PhD or equivalent industry/research experience in a computational, simulation, or systems engineering discipline.
• Proven expertise in areas such as CFD, FEA, computational mechanics, robotics, control systems, or signal processing.
• Hands-on experience with simulation, analytical, or technical modeling tools (e.g., ANSYS, Abaqus, MATLAB, OpenFOAM).
• Strong problem-solving and systems-thinking skills.
• Exceptional written and verbal communication skills.
• Proficiency with scripting and scientific workflows in Python and/or MATLAB.

Preferred Qualifications

• Experience applying AI/ML techniques within engineering domains or workflows.
• Familiarity with HPC, optimization, or large-scale simulation environments.
• Publication record, patents, or deep R&D involvement in computational engineering or simulation.

About the Company

micro1 connects domain experts to the development of frontier AI models. Real-world expertise is turned into training data, evaluations, and feedback loops that improve how models perform. AI labs and enterprises use micro1 to train models and build reliable AI agents through advanced evaluations and reinforcement learning environments.