Working cross-functionally across research, product, and client-facing teams, you will ensure models meet rigorous real-world engineering standards—not just theoretical benchmark metrics.

Execute Simulation Campaigns: Design and orchestrate large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).

Train & Validate Models: Train AI models on physics datasets and conduct rigorous evaluations of coverage, accuracy, and output quality against industrial validation standards.

Build Infrastructure & Tooling: Develop robust automated frameworks for dataset creation, simulation pipeline orchestration, and continuous model evaluation.

Integrate LLMs & Workflows: Architect agentic workflows and Retrieval-Augmented Generation (RAG) systems that seamlessly connect LLMs with engineering simulation pipelines.

Research Collaboration: Partner closely with the research team to analyze training runs, diagnose failure modes, and address data sparsity or architecture bottlenecks.

Technical Project Management: Lead research initiatives and manage technical communications with external engineering teams.

Core Qualifications

Education: Ph.D. or Master's degree in Machine Learning, Mechanical Engineering, Electrical Engineering, Computational Physics, Structural Mechanics, Semiconductor Engineering, or a related field.

Technical Mastery: Solid grounding in deep learning principles paired with a strong foundation in physics or engineering sciences.

Framework Proficiency: Hands-on experience implementing and training deep learning models.

Software Engineering: Demonstrated ability to write clean, maintainable Python in Linux and High-Performance Computing (HPC) environments.

MTS - AI Physics & Simulations

Responsibilities

Qualification

Required

Preferred

Company

Collinear AI

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