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About This Role
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.
What you'll be doing:
- 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
What we need to see:
- 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
Ways to stand out from the crowd:
- 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
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward\-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard\-working and talented people in the world working for us. If you're creative and passionate about developing cloud services we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD \- 287,500 USD for Level 4, and 224,000 USD \- 356,500 USD for Level 5\.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 15, 2026\.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Salary Context
This $184K-$356K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $190K across 251 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,133 AI roles we're tracking, AI Software Engineer positions make up 8% of the market. At NVIDIA, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 863 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($270K) sits 16% above the category median. Disclosed range: $184K to $356K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
NVIDIA AI Hiring
NVIDIA has 21 open AI roles right now. They're hiring across Research Scientist, AI Software Engineer, AI/ML Engineer, AI Product Manager. Positions span Santa Clara, CA, US, Austin, TX, US, Washington, DC, US. Compensation range: $224K - $488K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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