- Salary
- $170k–360k/yr
- Location
- Redwood City, CA
- Work type
- Hybrid
- Posted
- today
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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