Software Engineer - Data Infrastructure

Luma; Redwood City (CA); On-site; USD 150,000 - 240,000; Full time

Job summary

Luma is seeking a Data Infrastructure Engineer in our Research group to build and scale data pipelines for multimodal AI research and internal ML platforms. You will work closely with ML researchers and product teams to design reliable, high-performance data systems that accelerate experimentation and deployment.

The role emphasizes supporting complex ML workflows, not traditional product data infra, with a focus on scalable pipelines, monitoring, and cross-functional collaboration.

Qualifications

• Experience with high-throughput data infrastructure for ML workflows.
• Strong understanding of distributed systems and data engineering at scale.
• Ability to design and optimize data pipelines for ML research and internal teams.

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

Skills

Python

Distributed systems

ML data pipelines

Data engineering

Collaboration

Big data

Tools

Ray

Spark

Beam

Airflow

Job description

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