Simulation Runtime Software Engineer (Senior)

Vinci4d•Palo Alto, CA

1d•$220,000 - $265,000

About The Position

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. We are scaling deployment at industrial magnitude by increasing simulation throughput by two orders of magnitude, expanding simulation capabilities to maximize utility and domain coverage, and supporting global, multi-entity deployment across Tier-1 ecosystems. Our ambition is to become the default operator intelligence layer that hardware companies run on. The Simulation Runtime team focuses on mapping a computational problem to a runtime environment, making simulations run fast across heterogeneous compute platforms while retaining accuracy. This involves overcoming challenges like efficient data sharing and customizing inference and math to bespoke runtime hardware, ensuring Vinci Simulations run effectively regardless of the hardware, from a single desktop to multi-gpu clusters spanning global data center sites. This flexibility unlocks new utility for customers and powers larger, more useful applications.

Requirements

Deep understanding of scientific computing methods, boundary decomposition problems, and parallel computing.
Experience working on High Performance Computing runtime applications.
Experience with highly parallel computing frameworks (MPI, MPICH, ZMQ, OpenMP).
Experience with GPU Programming (Cuda, ROCM, Triton).
Have contributed to a production data processing system.
Familiarity with Statistical validation methods (Outlier detection, Bayes method, convergence criterion for nonlinear solvers).
Familiarity with ML basics (back prop, loss functions, generators, embeddings, transformer models).
Software engineering fundamentals.
Comfortable meeting software design standards to get code into a production environment.
A practical approach to prototyping necessary components that are currently missing.
Strong CI, regression testing, and validation discipline.
Comfort learning and evolving model deployment & runtime infrastructure.

Nice To Haves

Worked on highly performant deployed inference environments.
Shipped HPC library components.
Experience going from early stage prototype moving to a production environment.
Experience at a Startup or National Lab.
Experience with highly parallel ML training frameworks such as Ray.

Responsibilities

Design and implement low-latency, scalable solutions to decompose and distribute production simulations across multi-GPU machines, multi-node clusters, and networked nodes.
Ensure parallelism and correctness in simulation runtime solutions.