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Luma AI’s mission is to build Multimodal AGI
The Role
Lead global compute capacity and platform strategy for training and inference: plan multi-year capacity, manage vendor/cloud partnerships, direct infrastructure and datacenter teams, optimize cluster efficiency (>50% MFU), oversee large capital deployments, and serve as executive liaison to silicon vendors and hyperscalers to enable world-model and robotics workloads.
Summary Generated by Built In
The Role
Compute is the ultimate physical and financial prerequisite for the robotics foundation models we are building. This role owns Luma’s global compute footprint end-to-end—bridging macro capacity strategy, multi-million dollar capital allocation, and top-tier systems architecture. You will design our scaling roadmap from the silicon up, ensuring our research and robotics teams have the uninterrupted runway they need to ship frontier world models. As a member of the executive team, you will be the single person responsible for turning capital into capability.
What You'll Do
- Architect Multi-Year Compute Strategy: Lead capacity planning, global vendor and cloud partnerships, on-prem vs. cloud mix, and accelerator supply chain roadmaps (H/B-series GPUs, custom silicon evaluation).
- Direct the Platform Org: Provide strategic leadership to our infrastructure, distributed systems, and datacenter operations teams—scaling the organization to support next-generation compute demands.
- Maximize Fleet Utilization: Oversee the architectural efficiency of our cluster configurations to deliver >50% Model Flops Utilization (MFU) on flagship training runs.
- Command a Megawatt Budget: Negotiate, secure, and operate our largest-scale capital deployments for compute infrastructure, partnering directly with Finance to optimize unit economics and risk management.
- Unify Global Capacity: Champion the platform strategy that enables world-model training, heavy simulation rollouts, and real-time on-robot inference to seamlessly share a single, elastic fleet.
- Act as Principal Executive Interface: Serve as the primary commercial and strategic bridge to NVIDIA, AMD, hyperscalers, and frontier silicon vendors.
Qualifications:
- 10+ years of engineering leadership experience in large-scale distributed systems, infrastructure, or technical supply chain, with a proven track record of leading compute platform strategy at a frontier AI lab, hyperscaler, or major autonomy program.
- Deep technical & commercial fluency in high-performance cluster topology, high-speed interconnects (InfiniBand/RoCE), large-scale data systems, and the economics of distributed training architectures.
- Direct operational oversight of 10k+ accelerator environments in high-performance production settings.
Preferred qualifications:
- Scale Credentials: Experience orchestrating capital or infrastructure for training runs at the >100B-parameter or >100k-GPU-day scale.
- Robotics/Autonomy Context: Familiarity with the unique capacity and latency demands of edge-to-cloud inference and real-time autonomous systems.
The base pay range for this role is $250,000 – $450,000 per year.
About LumaLuma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.
Skills Required
- 10+ years engineering leadership in large-scale distributed systems, infrastructure, or technical supply chain
- Proven track record leading compute platform strategy at a frontier AI lab, hyperscaler, or major autonomy program
- Deep technical and commercial fluency in high-performance cluster topology, high-speed interconnects (InfiniBand/RoCE), and large-scale data systems
- Operational oversight of 10k+ accelerator environments in high-performance production settings
- Experience directing infrastructure, distributed systems, and datacenter operations teams at scale
- Experience orchestrating capital or infrastructure for >100B-parameter or >100k-GPU-day training scale
- Familiarity with robotics/autonomy capacity and edge-to-cloud inference latency requirements
Luma AI Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Luma AI and has not been reviewed or approved by Luma AI.
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Fair & Transparent Compensation — Pay is considered competitive for senior technical and some non-technical roles, with posted bands indicating strong market alignment in key locations. Publicly listed ranges provide directional clarity for certain roles and markets.
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Equity Value & Accessibility — Equity is positioned as a meaningful component of total compensation, and language in postings emphasizes ownership alongside cash pay. Signals indicate equity can be significant in senior roles where competition for talent is intense.
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Healthcare Strength — Core medical, dental, and vision coverage are referenced in multiple postings, aligning with standard expectations for venture-backed tech companies. These inclusions suggest baseline health benefits are part of the package.
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The Company
What We Do
Luma AI’s mission is to build Multimodal AGI: AI that can generate, understand, and operate in the physical world. We develop multimodal models across video, 3D, and generative media, and ship them in products like Dream Machine to help creators and teams turn ideas into compelling visuals—fast.
