Luma AI is the creative AI platform for video generation and image creation. Powered by the world's leading video generation models, Ray and Uni, and creative agents handling end-to-end workflows. Trusted by leading agencies and brands. Try it free. Backed by General Catalyst, a16z and CRV.
About the role
As a Data Infrastructure Engineer in Research at Luma, you will play a critical role in building and scaling the data infrastructure that supports our cutting-edge multimodal AI systems. Your work will focus on developing high-throughput, large-scale data processing pipelines tailored for machine learning research and internal ML platform needs.
What they're looking for
- Proficiency in Python (or similar languages with willingness to learn Python) and experience with large-scale, high-throughput data infrastructure
- Familiarity with distributed computing frameworks (e.g., Ray, Spark, Beam)
- Ability to design and optimize data pipelines for ML research and internal teams
- Strong problem-solving skills and understanding of data engineering at scale
- Collaborative, product-focused mindset, comfortable in fast-paced environments
- Experience sourcing, integrating, and optimizing data from diverse and large datasets
More about this role
As a Data Infrastructure Engineer in Research at Luma, you will play a critical role in building and scaling the data infrastructure that supports our cutting-edge multimodal AI systems. Your work will focus on developing high-throughput, large-scale data processing pipelines tailored for machine learning research and internal ML platform needs. You will collaborate closely with ML researchers and product teams to create reliable, efficient, and easy-to-use data infrastructure that empowers innovation and accelerates development. This role requires a strong foundation in distributed systems and data engineering, with an emphasis on supporting complex machine learning workflows rather than traditional product data infrastructure.
Build and maintain scalable data infrastructure for high-throughput machine learning workflows
Collaborate with ML researchers and product teams to ensure data systems meet evolving needs
Develop and optimize large-scale data pipelines and batch processing jobs
Contribute to the architecture and implementation of reliable, high-performance data platforms
Integrate open-source tools and continuously improve data infrastructure through monitoring and...
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