Software Engineer – Nonlinear Solid Mechanics & High-Performance Computing

Vinci4d•Palo Alto, CA

1d•$190,000 - $230,000•Hybrid

About The Position

At Vinci4d, we are building the next generation of simulation software for thermal, fluid flow, and structural mechanics applications — the kind of tools that change how engineers design products, from the first mesh to the final answer. We are a small, technically deep team that moves fast, ships real software, and takes on hard problems that matter. If you want your work to be foundational to a platform used by engineers worldwide, this is the place. We are looking for a software engineer who lives at the intersection of computational solid mechanics, numerical methods, and high-performance computing. You will design, implement, and tune solvers for geometric and material nonlinearity in solid mechanics — think large-deformation, contact, and history-dependent material response — that run at scale on modern hardware. You will write production-quality code, contribute to our CI/CD infrastructure, and collaborate closely with a multi-disciplinary team of physicists, engineers, and software developers. This is not a "maintain the existing stack" role. You will be building things that don't exist yet, solving problems that require both rigorous mathematical thinking and solid engineering instincts.

Requirements

Hands-on experience developing solvers for geometric and material nonlinearity in solid mechanics — large-deformation kinematics, nonlinear constitutive models, and the Newton-type schemes that drive them to convergence
Strong foundation in the finite element method (FEM) for solid and structural mechanics
Deep familiarity with iterative linear solvers (e.g., Krylov methods) and preconditioning techniques for large, sparse systems, with hands-on experience implementing these inside a nonlinear solver
Proven GPU programming experience (CUDA, HIP, SYCL, or similar) with a track record of getting real performance out of hardware
Proficiency in C++ and/or Python; comfort working in performance-critical codebases
Strong software engineering practices: Git workflows, code review, automated testing (unit, integration, regression), and CI/CD pipelines
3–6 years of industry or research experience in a relevant field (computational mechanics, scientific computing, computational physics, numerical simulation, or HPC)
A portfolio of work — open source contributions, published code, or shipped products — that demonstrates the above
A genuine collaborator: you learn from teammates as readily as you help them
Able to communicate technical depth clearly to people from different disciplines — physicists, mechanical engineers, product managers
Comfortable with ambiguity and excited by the challenges that come with building something new
Self-directed and ownership-oriented: you drive your work to completion without needing to be managed closely

Nice To Haves

Experience with warpage and residual-stress problems in semiconductor manufacturing (e.g., packaging, die/substrate stacks, thermomechanical deformation)
Familiarity with matrix-free methods for nonlinear and linear operator application
Experience with geometric multigrid approaches as solvers or preconditioners
Background in adaptive mesh refinement (AMR)
Familiarity with embedded geometry or immersed boundary methods for solid mechanics
Experience applying machine learning to solid mechanics problems (surrogates, constitutive modeling, solver acceleration)
Experience with performance profiling tools (Nsight, VTune, Roofline analysis)

Responsibilities

Develop and tune nonlinear solvers for solid mechanics, handling both geometric nonlinearity (large deformation, finite strain) and material nonlinearity (plasticity, viscoelasticity, temperature-dependent and history-dependent constitutive models)
Build and optimize the underlying linear algebra: iterative linear solvers and preconditioners for the large sparse systems arising at each Newton iteration
Port and optimize these solvers for GPU execution using CUDA, HIP, or equivalent frameworks, with a focus on memory bandwidth, occupancy, and scalability
Implement FEM discretizations for structural and thermomechanical field solves, with attention to robustness and convergence under stiff, ill-conditioned, and near-singular conditions
Contribute to a robust software engineering foundation: version control discipline, automated testing, CI/CD pipelines, and code review practices
Collaborate with domain experts to translate physical models and mathematical formulations into correct, efficient implementations
Profile and benchmark solver performance; identify and eliminate bottlenecks