--- title: 'Staff AI Infrastructure Engineer at Luma' canonical: 'https://feeny.ai/job/staff-ai-infrastructure-engineer-luma-sf-bay-area-dk5z25y116ez' type: 'job' last_seen: '2026-09-05' --- # Staff AI Infrastructure Engineer at Luma - **Company:** Luma - **Location:** Sf Bay Area, CA - **Compensation:** $230k–$360k - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-02-24 - **Last confirmed live:** 2026-09-05 - **Apply:** https://jobs.gem.com/lumalabs-ai/am9icG9zdDrkGd4GGu3apfJSBl9YCPbw ## Job description ## About Luma AI A new class of intelligence is emerging, systems that understand and generate the world across video, images, audio, and language. Building multimodal AGI is not just a modeling challenge. It is an infrastructure challenge at the edge of what hardware, software, and organizations can support. At Luma, we operate rapidly scaling 10k+ GPU fleets, pushing utilization, throughput, and reliability hard enough that yesterday’s solutions break regularly. Researchers depend on this infrastructure to move the frontier forward. Customers depend on it to power real creative work. Many companies run accelerators. Very few sit directly next to the teams inventing the models that redefine what those accelerators must do. At Luma, improvements to scheduling, efficiency, and reliability immediately translate into faster research iteration and entirely new product capabilities. We are still early. The playbook is still being written. A single exceptional engineer can reshape how the company operates. Where You Come In Our Infrastructure Engineering team is a systems engineering group with company-level responsibility. At Luma, reliability engineers work directly with the researchers and products pushing the limits of multimodal intelligence. We operate close to the metal: - Kernels - Containers - Schedulers - Networking - Storage - GPU behavior But we are also responsible for something bigger: Turning deep systems knowledge into repeatable, scalable reliability for the entire company. We are hiring a leader who will define that direction. You will be a technical authority, an organizational force multiplier, and a magnet for other great engineers. ## What You’ll Own Reliability of the Frontier - Architect and operate large, heterogeneous GPU environments under extreme demand - Improve utilization and performance where small gains materially change company outcomes - Resolve failures that span hardware, OS, runtimes, and orchestration - Eliminate entire classes of instability - Build mechanisms that make heroics unnecessary Scaling Training & Inference - Define how infrastructure and workloads evolve as cluster size and concurrency grow - Design scheduling, placement, and resource management approaches for increasingly complex jobs - Work directly with research to build the systems required for new model capabilities - Ensure inference platforms scale rapidly without sacrificing reliability or latency - Anticipate where today’s abstractions will fail and redesign ahead of them Building the Organization - Hire and develop exceptional systems and reliability engineers - Set the bar for technical depth, judgment, and production ownership - Shape architecture early through strong partnerships with research and product - Translate reliability constraints into long-term platform strategy ## Who You Are Required: - Deep expertise in Linux and distributed systems - Experience operating GPU / accelerator clusters in real production environments - Strong fluency in Kubernetes and modern open-source infrastructure - Comfortable debugging across hardware → kernel → runtime → orchestration - You understand how systems behave under contention and at scale - You write code and build automation - You think in bottlenecks, failure modes, and tradeoffs - Engineers trust your judgment, especially when things break Important: This role requires comfort operating close to upstream and close to the metal. If most of your experience has been inside highly abstracted internal platforms where others owned the underlying machinery, this is unlikely to be a match. Leadership Expectations - You raise reliability standards across the company - You influence product and research architecture early - You build strong partnerships, not ticket queues - You attract and level up exceptional engineers - You are curious how models use infrastructure, because improving systems expands what becomes possible ## Why This Role Is Special Most infrastructure roles optimize mature systems. This one helps define how reliability works for a new generation of AI infrastructure. The decisions you make here will influence: - How research progresses - How products scale - How customers trust us - And how the engineering organization grows If you want to build the reliability foundations of a company operating at the technological frontier, we should talk. ## About Luma ## Company Overview - **One-liner**: Luma builds multimodal AI models that can generate, understand, and operate in the physical world, shipping them in products like Dream Machine for creators and teams. - **Entity Type**: Private (Series C) - **Headquarters**: Redwood City, California, USA - **Founded**: 2021 - **Founders**: Not publicly listed on official sources ## Core Business - **Primary industry**: Artificial Intelligence / Generative AI / Creative AI - **Target customers**: B2B (enterprises, agencies, developers) and B2C (creators, filmmakers) - **Mission or purpose statement**: To build unified general intelligence that can generate, understand, and operate in the physical world. ## Products & Services - **Luma Dream Machine**: Consumer-facing product for turning ideas into compelling visuals (video, image, audio, text) using generative AI. - **Ray 3.2 API**: Production-grade video generation API with full creative control—multi-keyframe (up to 16), V2V up to 20 seconds, reframe, 1080p output, native HDR, and 16-bit EXR export. - **Uni-1.1 API**: Multimodal reasoning model API for image generation (text-to-image, reference-guided) and natural-language image editing, with up to nine reference inputs per request. - **Luma Agents**: Agentic creative workflows that plan, generate, iterate, and refine across every stage of creative work—available in the Luma App and trusted by teams at Publicis Groupe, Serviceplan, Mazda, Dentsu, and Humain. - **Open Physical AI Lab**: An open science effort to solve generalization in physical AI, built in the open for the benefit of all humanity. ## Market Standing - **Valuation/Market Cap**: Not disclosed - **Key Metrics**: Total Funding: $1.3B+ (official site) / $1.057B (LinkedIn); Annual Revenue: $5M (LinkedIn estimate); Users worldwide: 30M+; Team Members: 250+ - **Notable Investors/Partners**: HUMAIN (led Series C), Andreessen Horowitz (led Series B), Amplify Partners (led Series A), Matrix (led Seed Round). Partners include Envato, Comfy, Runware, Flora, Krea, Magnific, Fal, LovArt. - **Growth Signals**: 250+ team members, 30M+ users, $1.3B+ total funding, 45% yearly LinkedIn follower growth, recent product launches (Ray3.2, Uni-1.1, Luma Skills, Wonder Project partnership), $900M Series C raised November 2025. ## Competitive Advantages - **Full-stack AI**: Luma builds its own foundation models end-to-end, rather than wrapping third-party models—giving it unique control over quality, latency, and cost. - **Multimodal reasoning**: Uni-1 understands intention and responds to direction, not just prompts—offering brand intelligence at the model level. - **Cinematic-grade output**: Ray 3.2 delivers 1080p, native HDR, 16-bit EXR export—production-ready for professional film and advertising pipelines. - **Open science commitment**: The Open Physical AI Lab publishes research openly, attracting top-tier research talent and fostering community trust. - **Speed & cost**: Claims less than half the price and latency of comparable models. ## Strategic Focus - **Scaling enterprise adoption**: Custom deployment for creative teams at scale across advertising, film, and gaming production pipelines. - **Agentic workflows**: Building Luma Agents as a force multiplier for creative teams—automating repetitive tasks while maintaining brand consistency. - **Physical AI research**: Advancing the Open Physical AI Lab to solve generalization in physical AI, bridging digital generation with real-world operation. - **API ecosystem growth**: Expanding the developer platform with two tiers (Build and Scale) and SDKs for Python and JavaScript/TypeScript. ## Why Work Here - **Culture & mission**: "We believe real-world physics is the path to general intelligence. We unite research, product, and go-to-market into one engine." Described as a "lean, high-achieving team" building the future of creative intelligence. - **Compensation & benefits**: Competitive compensation, 100% covered medical/dental/vision for employee and family, $1,500/year learning stipend, catered meals, team events, home office setup. - **Work mode**: On-site roles in Redwood City, CA with additional offices in New York, NY; Los Angeles, CA; and international locations (Munich, Paris, London, Berlin). - **Engineering culture**: Emphasis on shipping frontier research directly into products—"frontier research ships straight into the hands of working creatives." Hiring across research, systems engineering, infrastructure, design, and product. - **Hiring process**: Transparent and fast—aims to complete within 2-3 weeks. Application review within a week, then recruiter screen, hiring manager interview, and team/leadership interviews. - **Employee ratings**: 4.1/5.0 on LinkedIn (9 reviews)—Work-Life: 3.6, Compensation: 4.4, Culture: 4.1, Career: 4.2. - **Open roles**: 46+ open positions including Forward Deployed Engineer, Research Scientist, Software Engineer, Robotics Engineer, Simulation Researcher, Site Reliability Engineer, Product Marketing Manager, Enterprise Account Executive, and more. ## Sources 1. [lumalabs.ai - Careers](https://lumalabs.ai/careers) 2. [lumalabs.ai - Official Site](https://lumalabs.ai/) 3. [lumalabs.ai - LLM Info](https://lumalabs.ai/llm-info) 4. [LinkedIn - Luma](https://linkedin.com/company/lumalabsai) ## Other roles at Luma - [Research Scientist / Engineer – Reinforcement Learning Infrastructure](https://feeny.ai/job/research-scientist-engineer-reinforcement-learning-infrastructure-luma-sf-bay-v3kqd6ee41ag) — Sf Bay Area, CA - [Tech Lead Manager, Inference](https://feeny.ai/job/tech-lead-manager-inference-luma-sf-bay-area-wv3qjs47nghh) — Sf Bay Area, CA - [Creative Technologist](https://feeny.ai/job/creative-technologist-luma-london-1edm2ph4be1t) — London, United Kingdom - [Sales Manager - West Coast](https://feeny.ai/job/sales-manager-west-coast-luma-los-angeles-qdhxhg06y85x) — Los Angeles, CA - [Forward Deployed Engineer (EU)](https://feeny.ai/job/forward-deployed-engineer-eu-luma-london-h5nr5x8w2bhd) — London, United Kingdom - [Product Manager, Applied Research](https://feeny.ai/job/product-manager-applied-research-luma-sf-bay-area-tgmbcragfeqt) — Sf Bay Area, CA - [Product Manager, Core Product](https://feeny.ai/job/product-manager-core-product-luma-sf-bay-area-dx7f4b6fnr4d) — Sf Bay Area, CA - [Social Media Editor](https://feeny.ai/job/social-media-editor-luma-los-angeles-ekyz2de2wh6f) — Los Angeles, CA - [Senior Video Editor](https://feeny.ai/job/senior-video-editor-luma-los-angeles-1ynx82dj6hbw) — Los Angeles, CA - [Product Marketing Manager](https://feeny.ai/job/product-marketing-manager-luma-sf-bay-area-s0n3bqsd0qy1) — Sf Bay Area, CA