Member of Technical Staff - Data Platform

SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.

ABOUT THE ROLE:

The Data Platform team builds and operates the infrastructure responsible for all large-scale data transport and processing across the company. We own and manage core systems including Apache Kafka, HDFS, Spark, Flink, and Trino, enabling real-time ML pipelines, feed ranking, experimentation, analytics, and observability at petabyte scale. Our team deals with latency-critical workloads, high-throughput streaming, and distributed compute systems that require fault tolerance, performance, and absolute reliability.

As a software engineer on the Data Platform team, you will design, build, and operate the distributed systems powering SpaceXAI's data movement and compute. You will take ownership of infrastructure components that process trillions of events daily, driving the scalability, performance, and reliability of the systems that power product and ML workloads across the company.

RESPONSIBILITIES:

• Design and implement high-throughput, low-latency data ingestion and transport systems.

• Scale and optimize multi-tenant Kafka infrastructure supporting real-time workloads.

• Extend and tune Spark, Flink, and Trino for demanding production pipelines.

• Build interfaces, APIs, and pipelines enabling teams to query, process, and move data at petabyte scale.

• Debug and optimize distributed systems, with a focus on reliability and performance under load.