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.
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.
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.
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
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- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
