The job, in the employer's words
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
Experience working in the robotics industry preferred
Requirements mentioned
- 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.
- Prior experience working with complex data infrastructure or AI/ML platforms highly desirable
- Experience with open source data infrastructure projects is a plus
- Experience working in the robotics industry preferred
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