The Role
The ML Engineer will integrate model architectures, optimize deployment workflows, maintain CI/CD pipelines, and ensure reliability of inference services across large-scale systems.
Summary Generated by Built In
About Luma AI
Luma’s mission is to build multimodal AI to expand human imagination and capabilities.
Role & Responsibilities
Background
Example Projects
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Compensation
Luma's mission is to build multimodal AI to expand human imagination and capabilities. 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.
Luma’s mission is to build multimodal AI to expand human imagination and capabilities.
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. 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 affect change. We know we are not going to reach our goal with reliable & scalable infrastructure, which is going to become the differentiating factor between success and failure.
- Ship new model architectures by integrating them into our inference engine
- Collaborate closely across research, engineering and infrastructure to streamline and optimize model efficiency and deployments
- Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows
- Automate, test and maintain our inference services to ensure maximum uptime and reliability
- Optimize deployment workflows to scale across thousands of machines
- Manage and optimize our inference workloads across different clusters & hardware providers
- Build sophisticated scheduling systems to optimally leverage our expensive GPU resources while meeting internal SLOs
- Build and maintain CI/CD pipelines for processing/optimizing model checkpoints, platform components, and SDKs for internal teams to integrate into our products/internal tooling
- Strong Python and system architecture skills
- Experience with model deployment using PyTorch, Huggingface, vLLM, SGLang, tensorRT-LLM, or similar
- Experience with queues, scheduling, traffic-control, fleet management at scale
- Experience with Linux, Docker, and Kubernetes
- Bonus points:
- Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink)
- Experience with high performance large scale ML systems (>100 GPUs)
- Experience with FFmpeg and multimedia processing
- Create a resilient artifact store that manages all checkpoints across multiple versions of multiple models
- Enable hotswapping of models for our GPU workers based on live traffic patterns
- Build a robust queueing system for our jobs that take into account cluster availability and user priority
- Architect a e2e model serving deployment pipeline for a custom vendor
- Integrate our inference stack into an online reinforcement learning pipeline
- Regression & precision testing across different hardware platforms
- Building a full tracing system to trace the end-to-end lifetime of any inference workload
- Python
- Redis
- S3-compatible Storage
- Model serving (one of: PyTorch, vLLM, SGLang, Huggingface)
- Understanding of large-scale orchestration, deployment, scheduling (via Kubernetes or similar)
- CUDA
- FFmpeg
The base pay range for this role is $187,500 – $395,000 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
- Strong Python and system architecture skills
- Experience with model deployment using PyTorch, Huggingface, vLLM, SGLang, tensorRT-LLM, or similar
- Experience with Linux, Docker, and Kubernetes
- Experience with queues, scheduling, traffic-control, fleet management at scale
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.
