MTS - AI Physics & Simulations

Collinear AI; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Collinear.AI is seeking a Member of Technical Staff (Applied Scientist) to advance AI-accelerated simulation. You will create AI physics models and work on CFD, structural mechanics, and multi-physics problems across production-grade pipelines.

You will collaborate with customers and research teams, train physics-informed models, and deploy solutions to engineering teams. This role emphasizes rigorous engineering standards and cross-functional impact.

Qualifications

• Ph.D. or Master’s degree in ML or engineering fields.
• Solid grounding in deep learning and physics/engineering sciences.
• Hands-on experience implementing and training deep learning models.
• Proficiency in Python in Linux and HPC environments.
• Strong verbal and written communication; self-directed and collaborative.

Responsibilities

• Execute large-scale simulation campaigns using domain solvers like OpenFOAM, ANSYS, COMSOL, Abaqus.
• Train AI models on physics datasets and evaluate coverage, accuracy and quality.
• Build robust automation frameworks for datasets, pipelines, and model evaluation.
• Architect agentic workflows and RAG systems linking LLMs with simulation pipelines.
• Collaborate with research teams to analyze runs, diagnose failures, and address bottlenecks.
• Lead research initiatives and manage technical communications with external teams.

Skills

Deep learning principles

Physics or engineering sciences

Python

Linux & HPC environments

Communication skills

Ownership mindset

Education

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

Tools

OpenFOAM

ANSYS

COMSOL

Abaqus

Job 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.