The role involves building and scaling data infrastructure for multimodal AI systems, developing high-throughput data pipelines, and collaborating with ML researchers and product teams to enhance data systems.
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.
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. 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.
Responsibilities
- 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 tuning
- Participate in cross-functional projects to improve data reliability, scalability, and operational excellence
- Support the evaluation and adoption of new programming languages and frameworks relevant to data infrastructure
- Engage in continuous improvement of data infrastructure through monitoring, troubleshooting, and performance tuning
- Collaborate with research & engineering teams to help define and refine best practices for data infrastructure development
Qualifications
- 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
- Comfortable working in a fast-paced, product-focused environment with a strong execution mindset
- Open to candidates across seniority levels, from mid-level individual contributors to senior engineers and managers.
Nice to have
- Prior experience working with complex data infrastructure or AI/ML platforms highly desirable
- Experience with open source data infrastructure projects is a plus
Top Skills
Beam
Python
Ray
Spark
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