This listing pays about $265,000 a year. Across 754 open software engineer listings that state pay, the middle half runs $157,500 to $251,500 and the median is $197,500. That is 34% above the median, above the middle half of those listings.

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About the job

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