# MTS - AI Physics & Simulations at Collinear AI

MTS - AI Physics & Simulations at Collinear AI in San Francisco, CA and 1 more. On-site, full-time. Posted September 14, 2026.

- Company: Collinear AI
- Location: San Francisco, CA and 1 more
- Workplace: On-site
- Job type: Full-time
- Posted: 2026-09-14
- Apply: https://jobs.ashbyhq.com/collinear-ai/e1a733d5-2b9c-41d2-a1e3-2e0df1aabae4/application
- Source: https://jobmagic.ai/jobs/mts-ai-physics-simulations-collinear-ai-vvf0es

## Requirements

- 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.

## Description

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.