Collinear AI

Collinear AI

MTS - AI Physics & Simulations

San Francisco, CA

Sponsorship not specifiedDetected 39 days ago
PythonFull-Stack DevelopmentCI/CDLinuxMachine LearningDeep LearningLLMsRAGProject ManagementANSYSCFDMechanical DesignElectrical EngineeringCOMSOLResearchCommunicationCollaboration

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Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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About the role

  • 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.
  • 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.
  • 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.

Responsibilities

  • Execute Simulation Campaigns: Design and orchestrate large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
  • Build Infrastructure & Tooling: Develop robust automated frameworks for dataset creation, simulation pipeline orchestration, and continuous model evaluation.
  • 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.
  • 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.
  • Key target domains include computational fluid dynamics (CFD), structural mechanics, semiconductor design, multi-physics modeling, and digital twins.
  • Design and orchestrate large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
  • Train & Validate Models:
  • Build Infrastructure & Tooling:
  • Develop robust automated frameworks for dataset creation, simulation pipeline orchestration, and continuous model evaluation.

Requirements

  • Framework Proficiency:
  • Demonstrated ability to write clean, maintainable Python in Linux and High-Performance Computing (HPC) environments.
  • Outstanding verbal and written communication skills, with the ability to explain complex technical concepts to both specialized engineers and non-technical stakeholders.
  • Ph.D. or Master's degree in Machine Learning, Mechanical Engineering, Electrical Engineering, Computational Physics, Structural Mechanics, Semiconductor Engineering, or a related field.
  • Solid grounding in deep learning principles paired with a strong foundation in physics or engineering sciences.
  • Hands-on experience implementing and training deep learning models.
  • 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.

Nice to have

  • Hands-on industrial or academic experience with simulation solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
  • Track record of automating large-scale simulation workloads on HPC clusters.
  • Meaningful contributions to large-scale open-source projects or production codebases.
  • Strong software engineering discipline, including static typing, unit testing, and CI/CD maintenance.

Benefits

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

Company info

  • In this role, you will collaborate with customers and internal research teams to build, test, and deploy AI Physics Models.
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This listing is sourced directly from Collinear AI's careers page and normalized into a canonical job model.