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Designs, builds, and operates backend services and infrastructure for a production simulation and inference platform. Responsibilities include Terraform-managed cloud environments, CI/CD pipelines, build systems for Python and C++ codebases, infrastructure hardening, and taking research prototypes into reliable customer-facing production systems. The role partners with physicists, AI researchers, software engineers, and computational geometry experts while supporting large-scale scientific and machine learning workloads.

Validate production physics simulation systems by building runtime evaluation mechanisms and data-driven validation approaches. Collaborate with physicists, AI researchers, software engineers, and computational geometry experts to evaluate machine learning and solver solutions. Define implementable architectures, run large-scale simulations, identify failures, and establish CI, regression testing, and validation practices for production software.

Build and simulate thermo-mechanical models for customer hardware designs, evaluating warpage, stress, reliability, nonlinear behavior, and contact interactions. Support product demonstrations, proof-of-concept evaluations, onboarding, training, troubleshooting, and technical workshops. Collaborate with customer engineering, sales, product, and engineering teams to communicate simulation results, identify workflow challenges, improve the product, and represent the company at industry events.

Develop and productionize machine learning applications for physics simulation, including transient modeling, temporal propagation, geometry meshing, and simulation acceleration. Generate and curate training and verification data, prototype and validate new capabilities, and collaborate with physicists, AI researchers, software engineers, and computational geometry experts to deliver customer-ready solutions. Contribute to production data systems, model infrastructure, testing, validation, and software engineering practices.

Own production release lifecycles and deployment decisions while designing and executing system integration and automated tests. Build Python and Playwright test frameworks for web and physics-AI systems, validate numerical outputs against FEA benchmarks, champion test-driven development, coordinate releases, and translate technical changes into customer-facing release notes.