Collinear.AI is seeking a Member of Technical Staff (Applied Scientist) with deep expertise in engineering sciences to work at the frontier of AI-accelerated simulation. In this role, you will collaborate with customers and internal research teams to build, test, and deploy AI Physics Models.

You will contribute across the full stack: curating high-fidelity simulation datasets, training and evaluating physics-informed models, and delivering production-grade AI solutions directly to engineering teams. Key target domains include computational fluid dynamics (CFD), structural mechanics, semiconductor design, multi-physics modeling, and digital twins.

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.

Key Responsibilities

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.

Communication: Outstanding verbal and written communication skills, with the ability to explain complex technical concepts to both specialized engineers and non-technical stakeholders.

Ownership & Mindset: Self-directed operator who thrives with autonomy, maintains a low-ego approach to collaboration, and excels in fast-paced environments at the intersection of simulation and ML.

Preferred Qualifications

Hands-on industrial or academic experience with simulation solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).

Direct experience applying machine learning to physics simulations or surrogate modeling (e.g., Neural Operators, Physics-Informed Neural Networks).

Track record of automating large-scale simulation workloads on HPC clusters.

Meaningful contributions to large-scale open-source projects or production codebases.

Published research in top-tier machine learning (NeurIPS, ICLR, ICML) or computational engineering conferences/journals.

Strong software engineering discipline, including static typing, unit testing, and CI/CD maintenance.