Flexcompute Inc.
Computational Mechanics Scientist (Structural Solver)
Flexcompute Inc., Watertown, New York, United States
About Flexcompute
We are a “Physics Intelligence” company making hardware innovation as easy as software. Our solvers (Flow360, Tidy3D) are not just faster; they are effectively instant, reducing simulation times from days to minutes using massive GPU parallelism and proprietary algorithms.
Intellectual Density: We maintain an exceptionally high talent bar. Our team of ~90 includes over 50 PhDs from MIT, Stanford, and top research labs. You will work alongside peers who will challenge your thinking and elevate your work daily.
Impact: Our technology is currently used to design optical interconnects, 3D chiplets, quantum computing chips, eVTOL aircraft, and next-gen wind turbines. You are building the “math engine” that drives real-world engineering breakthroughs.
Stability & Growth: We are a Series C stage company with significant funding, revenue-generating products, and a clear path to disrupting the $20B+ simulation market.
Education: Ph.D. in Applied Mechanics, Physics, Applied Mathematics, or a related field.
Deep Physics Intuition: You possess an intuitive grasp of continuum mechanics—elasticity, plasticity, contact mechanics, and wave propagation. You can predict how a structure should behave before the simulation runs.
Advanced Numerical Analysis: You have a theoretical mastery of numerical PDEs. You can mathematically prove the consistency, stability, and convergence of a scheme. You understand the trade-offs between implicit/explicit integration and various spatial discretizations.
Strong General Programming: Proficiency in at least one high-level language (C++, Python, Julia, Rust, or Fortran) with a focus on algorithmic efficiency. You understand data structures, computational complexity (Big O), and how to write maintainable research code.
First-Principles Thinking: Demonstrated ability to solve novel problems where no textbook solution exists.
Preferred (But Not Required)
Interest in learning High-Performance Computing (HPC) and GPU architecture.
Experience with C++ for scientific computing.
Exposure to multi-physics coupling (fluid-structure interaction, thermal-stress).
Key Responsibilities
Solver Formulation: Derive and implement governing equations for structural mechanics, ensuring thermodynamic consistency and physical accuracy across linear and non-linear regimes.
Numerical Architecture: Design the core numerical framework. You will determine the optimal discretization strategies (FEM, DG, IGA, etc.) to balance stability, accuracy, and computational cost.
Algorithmic Development: Specific language is secondary to code quality. You will write clean, modular, and efficient algorithms to solve large-scale systems of equations.
Physics-Driven Validation: Diagnose solver behavior not just as code execution, but as physical modeling. You must distinguish between numerical instability and physical phenomena.
Collaborative Autonomy: We operate with high trust. You own your module, but you have immediate access to world-class experts in electromagnetics and fluid dynamics for cross-domain problems.
Continuous Learning: We host regular internal seminars where team members teach each other—from “Advanced Linear Algebra” to “GPU Memory Hierarchies.”
Compensation: Competitive salary + meaningful equity in a fast-growing startup.
Well-being: Comprehensive Medical, Dental, and Vision insurance; 401(k); and a gym/fitness allowance.
Environment: A flat hierarchy where the “best idea wins,” regardless of job title.
#J-18808-Ljbffr
Intellectual Density: We maintain an exceptionally high talent bar. Our team of ~90 includes over 50 PhDs from MIT, Stanford, and top research labs. You will work alongside peers who will challenge your thinking and elevate your work daily.
Impact: Our technology is currently used to design optical interconnects, 3D chiplets, quantum computing chips, eVTOL aircraft, and next-gen wind turbines. You are building the “math engine” that drives real-world engineering breakthroughs.
Stability & Growth: We are a Series C stage company with significant funding, revenue-generating products, and a clear path to disrupting the $20B+ simulation market.
Education: Ph.D. in Applied Mechanics, Physics, Applied Mathematics, or a related field.
Deep Physics Intuition: You possess an intuitive grasp of continuum mechanics—elasticity, plasticity, contact mechanics, and wave propagation. You can predict how a structure should behave before the simulation runs.
Advanced Numerical Analysis: You have a theoretical mastery of numerical PDEs. You can mathematically prove the consistency, stability, and convergence of a scheme. You understand the trade-offs between implicit/explicit integration and various spatial discretizations.
Strong General Programming: Proficiency in at least one high-level language (C++, Python, Julia, Rust, or Fortran) with a focus on algorithmic efficiency. You understand data structures, computational complexity (Big O), and how to write maintainable research code.
First-Principles Thinking: Demonstrated ability to solve novel problems where no textbook solution exists.
Preferred (But Not Required)
Interest in learning High-Performance Computing (HPC) and GPU architecture.
Experience with C++ for scientific computing.
Exposure to multi-physics coupling (fluid-structure interaction, thermal-stress).
Key Responsibilities
Solver Formulation: Derive and implement governing equations for structural mechanics, ensuring thermodynamic consistency and physical accuracy across linear and non-linear regimes.
Numerical Architecture: Design the core numerical framework. You will determine the optimal discretization strategies (FEM, DG, IGA, etc.) to balance stability, accuracy, and computational cost.
Algorithmic Development: Specific language is secondary to code quality. You will write clean, modular, and efficient algorithms to solve large-scale systems of equations.
Physics-Driven Validation: Diagnose solver behavior not just as code execution, but as physical modeling. You must distinguish between numerical instability and physical phenomena.
Collaborative Autonomy: We operate with high trust. You own your module, but you have immediate access to world-class experts in electromagnetics and fluid dynamics for cross-domain problems.
Continuous Learning: We host regular internal seminars where team members teach each other—from “Advanced Linear Algebra” to “GPU Memory Hierarchies.”
Compensation: Competitive salary + meaningful equity in a fast-growing startup.
Well-being: Comprehensive Medical, Dental, and Vision insurance; 401(k); and a gym/fitness allowance.
Environment: A flat hierarchy where the “best idea wins,” regardless of job title.
#J-18808-Ljbffr