Senior Software Engineer, AI Speed Infrastructure

NVIDIA•Redmond, CA

About The Position

With the latest advances in AI, verification and feedback loops are becoming the clock speed of software development. We are looking for someone who wants to build AI speed infrastructure for Tegra: a ridiculously fast build, test, and validation system for the future, aimed at supporting developers and swarms of agents where turn times will be measured in seconds. We are looking for someone passionate about building the build, test, and validation platform for our Tegra driver developers: someone who can design workflows, build the CI control plane, and dramatically improve development speed and signal quality across large, safety-critical C/C++ codebases. The right person will treat build graphs, test selection, cacheability, hermeticity, hardware scheduling, policy, and developer experience as first-class product surfaces, turning them into workflows teams actually want to use and integrating AI agents into the control plane to enable self-healing and further optimizations. This is fundamentally a performance engineering role. You will be ruthlessly optimizing every step of the pipeline—rebuilding infrastructure from the ground up wherever needed—to define how code moves from a developer's (or agent's) change to a trusted, merged signal. We want someone who can design fully automated, multi-tiered testing pipelines where code is seamlessly validated across local, remote, and scarce device-lab resources. You will architect how risk-based gating operates at agent-speed, how failures are autonomously diagnosed and remediated, how infrastructure self-heals, and how we scale these next-generation workflows across massive engineering teams.

Requirements

BS/MS in Electrical or Computer Engineering, or equivalent experience
8+ years of relevant industry experience
Strong systems / build architecture for C/C++ codebases
Real daily use of AI coding tools and agent workflows
Deep experience with build systems, CI orchestration, and test strategy
Python / scripting for workflow automation, tooling, and agents
Ability to reason about safety-critical or high-assurance validation flows

Nice To Haves

Built or scaled CI/build systems for large mono-repos or embedded platforms
Improved feedback loops with measurable gains in build/test latency or signal quality
Built speed infrastructure for AI coding agents, autonomous validation, or high-throughput CI users
Worked on hardware/device-lab testing, remote execution, or change-based test selection
Shipped internal AI-assisted developer workflows used by other engineers

Responsibilities

Build AI-native, self-healing CI workflows for large Tegra C/C++ codebases
Design fast build, test, and validation paths that keep developers and agents in tight loops
Integrate reasoning agents that can triage failures, recommend fixes, and safely automate routine recovery
Improve incremental builds, caching, remote execution, and test selection
Integrate simulation, emulation, and device-backed testing into trustworthy CI
Build machine-readable outputs, policies, and recovery paths that agents can safely use at scale
Package workflows and platform capabilities so other teams can reuse them
Drive adoption with metrics, docs, and hands-on enablement