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About This Role
At NVIDIA Lightspeed Studios we are passionate about pushing the limits of technology, and we're looking for a Senior Software Engineer to join our team! We help shape the future of games by combining the best AI and graphics technology NVIDIA has to offer with the most advanced games and tools in the industry. In this role, you'll help take new, unannounced projects powered by state-of-the-art AI models from research to real-time, interactive gaming experiences. If you're passionate about bringing generative AI to games, we'd love to hear from you!
Some existing projects we've been working on:
- RTX Remix is an open-source modding platform for remastering classic games with full path tracing, DLSS 4.5, and generative AI tools that turn low-resolution textures into physically based materials. Newer features like RTX Remix Logic and Advanced Particle VFX let modders add dynamic, gameplay-driven effects: https://www.nvidia.com/en-us/geforce/rtx-remix/
- Zorah is our neural rendering tech demo for the GeForce RTX 50 Series launch at CES 2025. It features massive, fully path-traced scenes in Unreal Engine 5, powered by RTX Neural Shaders, RTX Mega Geometry, and DLSS 4: https://developer.nvidia.com/rtx-kit
- Project R2X is a vision-enabled AI avatar for RTX PCs that can see the screen, assist with apps, and summarize documents. It is rendered with RTX Neural Faces and animated by a diffusion-based Audio2Face-3D model: https://nvidianews.nvidia.com/news/nvidia-launches-ai-foundation-models-for-rtx-ai-pcs
- NVIDIA AI for Gaming brings conversational, autonomous game characters to life with small on-device models for speech, intelligence, and animation. It powers AI teammates in PUBG: BATTLEGROUNDS, AI citizens in inZOI, and an in-game advisor in Total War: PHARAOH: https://developer.nvidia.com/ace-for-games
What you'll be doing:
- Build and ship software for upcoming, not-yet-announced projects that bring world models and video diffusion models to real-time gaming, from prototype to release.
- Train, fine-tune, and evaluate models, including data curation and adapting models to game-specific content and controls.
- Optimize models and inference for latency, token throughput, and quality, using techniques such as distillation, quantization, and reduced-step sampling on RTX devices and in the cloud.
- Profile and remove bottlenecks across the stack, from model architecture to GPU kernels.
- Integrate models into Unreal Engine, Unity, and custom engines, so they run efficiently alongside rendering.
- Lead technical decisions, mentor other engineers, and collaborate with NVIDIA Research, rendering, and art teams.
What we need to see:
- BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
- 12+ years of software engineering experience, including substantial hands-on work shipping AI, deep learning, or machine learning systems.
- Expert-level C++ and Python.
- Hands-on experience training and fine-tuning deep learning models with PyTorch or a similar framework, including distributed training.
- Experience in one or more of these areas: video generation, world models, diffusion or flow-matching models, transformers, or neural rendering.
- Experience optimizing GPU inference with CUDA, TensorRT, TensorRT-LLM, or Triton.
- A track record of turning complex prototypes into shipped products, and clear communication across research, engineering, and art.
Ways to stand out from the crowd:
- Hands-on experience with interactive, action-conditioned, or real-time world models or video generation.
- Experience developing custom CUDA or Triton kernels.
- Experience with game engine development or real-time rendering pipelines.
- Open-source contributions (such as Diffusers, FastVideo, vLLM, SGLang, or TensorRT-LLM), publications, or patents.
If you're motivated to join the Lightspeed Studios Engineering team, please submit your resume and a cover letter explaining your proven experience and why you'd be a phenomenal fit for NVIDIA Lightspeed. We look forward to reviewing your application and potentially welcoming you to our team.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 2, 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 $224K-$431K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $193K across 172 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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% 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 $220,000 based on 1,448 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($327K) sits 49% above the category median. Disclosed range: $224K to $431K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NVIDIA AI Hiring
NVIDIA has 52 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span Santa Clara, CA, US, Austin, TX, US, Santa Clara, CA. Compensation range: $147K - $448K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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