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Physics Applications - Software Engineer (Principal)

External
vinci4d logoVinci4d · Palo Alto
$210K–$285K/yrFull-timeRemote4d ago
CI/CDCore MLLeadershipMachine LearningNumPyPerformance Optimization
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Responsibilities

  • Your north star will be production and delivering value to our customers while establishing and maintaining the technical integrity of our codebase.
  • You will work with Physicists, AI researchers, Software Engineers and Computational Geometry experts. You will collaborate closely with this team to enforce software engineering best practices throughout the full development lifecycle, from ideation to deployment.

Requirements

  • 8+ years of experience in high-quality software development, with significant experience designing and building production-grade systems.
  • Prior experience with scientific computing or physics simulators (FEM, FEA, Molecular Dynamics, FDTD), or large-scale machine learning systems.
  • STEM MSc, PhD preferred but not required.
  • Demonstrated ability to lead technical initiatives focused on code health, modularity, and system correctness.
  • Expertise in building robust, tested, and maintainable software libraries and APIs.
  • Strong proficiency in modern software development practices, including system design, agentic coding, testing frameworks, and continuous integration/delivery (CI/CD).
  • Have contributed to a production data processing system.
  • We are very excited to talk with you if you have
  • Experience building and maintaining core ML, data infrastructure, or numerical computing software (e.g., PyTorch, Numpy, Cuda, distributed systems).
  • Experience going from early stage prototype moving to a production environment
  • At a Startup or National Lab
  • Have leveraged simulation for design or data generation purposes.
  • Engineering Expectations
  • Architectural Leadership: Define and uphold rigorous software design standards to ensure the code base remains clean, modular, and scalable in a production environment.
  • Code Health and Testability: Drive strong CI, comprehensive regression testing, and validation discipline across all components.
  • Technical Ownership: Capable of independently solving complex architectural problems and taking ownership of core model infrastructure evolution.
  • Mentorship: Mentor scientists & engineers on best practices, performance optimization, and system design.
  • Why Vinci
  • Join a rare early-stage startup that has successfully moved a foundational product from research to real-world, production environments, already serving Tier-1 semiconductor and hardware customers.
  • Our Mission & Impact
  • Growth & Opportunity
  • This is a unique opportunity

Benefits

Health insurance

Additional Information

The Mission At Vinci, we are building the operator intelligence infrastructure that modern hardware programs rely on daily. We have already proven that a single foundation model works out of the box across physics on realistic production workloads. Trained on PetaBytes of structured physics data Running billion-voxel inference in production Tier-1 semiconductor and hardware customers Operating across multiple physical scales and operator regimes We are scaling deployment at industrial magnitude: Increase simulation throughput by two orders of magnitude Expand simulation capabilities to maximize utility and domain coverage Support global, multi-entity deployment across Tier-1 ecosystems Our ambition is to become the default operator intelligence layer that hardware companies run on. Solving Across Space and Time Our proven unified model architecture allows users to rapidly obtain steady state solutions of various partial differential equations. We are expanding this capability to support new physics, new geometries. Beyond that we are building out transient solutions, modeling interactions, deformation and dynamics. A core challenge as we scale out support is designing simple and clean interfaces that turn portions of the codebase into a clean library, ensuring they are correct and accurately reflect the underlying physics being modeled. These are promising applications where Vinci's approach can not only reduce the compute load but also achieve greater accuracy.


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