The Role
The Data Reliability Engineer will enhance the resilience and scalability of data infrastructure, focusing on automation and reliability. Responsibilities include managing data pipelines, operating Kubernetes clusters, and defining observability standards.
Summary Generated by Built In
About Luma AI
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.
Where You Come In
As our models scale to "omni" capabilities, our data infrastructure must be unbreakable. We are looking for a Data Reliability Engineer who brings a Site Reliability Engineering (SRE) mindset to the world of massive-scale data. You will be responsible for the resilience, automation, and scalability of the petabyte-scale pipelines that feed our research. This is not just about keeping the lights on; it’s about treating infrastructure as code and building self-healing data systems that allow our researchers to train on massive datasets without interruption. Whether you are a junior engineer with a passion for automation or a seasoned SRE veteran, you will play a critical role in hardening the backbone of Luma’s intelligence.
What You'll Do
- Automate Everything: Apply Infrastructure-as-Code (IaC) principles using Terraform to provision, manage, and scale our data infrastructure.
- Harden Data Pipelines: Build reliability and fault tolerance into our core data ingestion and processing workflows, ensuring high availability for research jobs.
- Scale Kubernetes & Ray: Operate and optimize large-scale Kubernetes clusters and Ray deployments to handle bursty, high-throughput workloads.
- Define Reliability: Establish Service Level Objectives (SLOs) and observability standards (Prometheus/Grafana) for our data platforms.
- Debug & Heal: serve as the first line of defense for complex infrastructure failures, diagnosing root causes in distributed storage and compute systems.
Who You Are
- Deep SRE/DevOps proficiency: You live and breathe Linux, networking, and automation.
- Infrastructure-as-Code Native: You have extensive experience with Terraform, Ansible, or similar tools to manage complex cloud environments (AWS/GCP).
- Kubernetes Expert: You have managed Kubernetes in production and understand its internals, not just how to deploy containers.
- Python Proficiency: You can write high-quality Python code for automation, tooling, and infrastructure management.
- Data-Minded: You understand the specific challenges of stateful data systems and high-throughput storage (S3/Object Store).
What Sets You Apart (Bonus Points)
- Experience managing GPU clusters or AI/ML workloads.
- Background in both Software Engineering and Operations (DevOps).
- Experience with high-performance networking (InfiniBand/RDMA).
The base pay range for this role is $170,000 – $360,000 per year.
Top Skills
Grafana
Kubernetes
Prometheus
Python
Ray
Terraform
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
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.
