{"id":708349,"url":"https://alion.io/job/vinci4d-software-engineer-nonlinear-solid-mechanics-high-performance-computing","title":"Software Engineer – Nonlinear Solid Mechanics & High-Performance Computing","company":{"id":716702,"name":"Getvinci","domain":"getvinci.ai","url":"https://alion.io/company/getvinci","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":90,"open_postings":10,"ghost_share":0,"stale_share":0.2,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":190000,"max":230000,"currency":"USD","period":"year","gross":null,"usd_annual":230000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"CUDA","optional":false},{"name":"Git","optional":false},{"name":"HPC","optional":false},{"name":"Python","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-06-24T21:01:56Z","employer_posted_date":"2026-06-24","last_verified_at":"2026-11-04T00:49:13Z","board_verified":true,"closed_at":null,"days_open":132,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":131},"description":"About Us\nAt 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.\nThe Role\nWe 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.\nThis 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.\nWhat You Will Work On\nDevelop 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)\n\nBuild and optimize the underlying linear algebra: iterative linear solvers and preconditioners for the large sparse systems arising at each Newton iteration\n\nPort and optimize these solvers for GPU execution using CUDA, HIP, or equivalent frameworks, with a focus on memory bandwidth, occupancy, and scalability\n\nImplement FEM discretizations for structural and thermomechanical field solves, with attention to robustness and convergence under stiff, ill-conditioned, and near-singular conditions\n\nContribute to a robust software engineering foundation: version control discipline, automated testing, CI/CD pipelines, and code review practices\n\nCollaborate with domain experts to translate physical models and mathematical formulations into correct, efficient implementations\n\nProfile and benchmark solver performance; identify and eliminate bottlenecks\n\nWhat We Are Looking For\nTechnical Skills\nHands-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\n\nStrong foundation in the finite element method (FEM) for solid and structural mechanics\n\nDeep 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\n\nProven GPU programming experience (CUDA, HIP, SYCL, or similar) with a track record of getting real performance out of hardware\n\nProficiency in C++ and/or Python; comfort working in performance-critical codebases\n\nStrong software engineering practices: Git workflows, code review, automated testing (unit, integration, regression), and CI/CD pipelines\n\nExperience\n3-6 years of industry or research experience in a relevant field (computational mechanics, scientific computing, computational physics, numerical simulation, or HPC)\n\nA portfolio of work - open source contributions, published code, or shipped products - that demonstrates the above\n\nSoft Skills\nA genuine collaborator: you learn from teammates as readily as you help them\n\nAble to communicate technical depth clearly to people from different disciplines - physicists, mechanical engineers, product managers\n\nComfortable with ambiguity and excited by the challenges that come with building something new\n\nSelf-directed and ownership-oriented: you drive your work to completion without needing to be managed closely\n\nNice to Have\nExperience with warpage and residual-stress problems in semiconductor manufacturing (e.g., packaging, die/substrate stacks, thermomechanical deformation)\n\nFamiliarity with matrix-free methods for nonlinear and linear operator application\n\nExperience with geometric multigrid approaches as solvers or preconditioners\n\nBackground in adaptive mesh refinement (AMR)\n\nFamiliarity with embedded geometry or immersed boundary methods for solid mechanics\n\nExperience applying machine learning to solid mechanics problems (surrogates, constitutive modeling, solver acceleration)\n\nExperience with performance profiling tools (Nsight, VTune, Roofline analysis)\n\nWhy Vinci4d\nWork on genuinely hard technical problems with real engineering impact\n\nJoin a small team where your contributions are visible and your voice is heard\n\nCompetitive compensation with equity participation\n\nFlexible work environment\n\nThe satisfaction of building something from the ground up - 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