Sr. Software Engineer (AI Data Engineering)

D8 LooP; Hawthorne (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

SpaceX in Hawthorne, CA is seeking a Software Engineer (AI Data Engineering) to develop data pipelines, dashboards, and tooling for mission-critical AI workloads. You will validate AI outputs, integrate tools into CI/CD, and work within Linux-based environments to ensure robust, testable systems.

Ideal candidates have strong Python and SQL skills, experience with Airflow, and familiarity with containerized deployment using Docker and Kubernetes.

Qualifications

• Proficiency in Python programming for data engineering tasks.
• Strong SQL skills across relational and non-relational databases.
• Experience with orchestration and data workflows (Airflow).
• Capability to integrate tools into CI/CD pipelines and review processes.
• Proficient in Git-based development and Linux environments.

Responsibilities

• Develop data pipelines and data-centric software for AI workflows.
• Build internal dashboards to monitor mission data and tool results.
• Test and validate AI outputs against known-good results.
• Prepare CI/CD pipelines to automate deployment and validation.

Skills

Python

SQL

Airflow

CI/CD

Git

Linux

AI data pipelines

Testing AI outputs

Tools

Docker

Kubernetes

Job description

SpaceX · boards.greenhouse.io · checked today

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars. SOFTWARE ENGINEER (AI DATA ENGINEERING)

Skills, with evidence

• Python 5+ years of programming experience in Python must have
• SQL Working knowledge of relational (PostgreSQL), non-relational, and vector databases, with strong SQL skills must have
• Familiarity deploying containerized applications using Docker and Kubernetes not practised here
• Thoroughly test and validate all tools, including evaluating AI outputs against known-good results, so engineers can trust them in a flight-critical environment
• Airflow / orchestration Integrate tools into CI/CD pipelines, mission workflows, and review/sign-off processes so automation runs where engineers already work
• Build internal web dashboards that give engineers a clear view of mission status, open work, and data review results not practised here
• Built code or configuration generators that turn a source of truth (design databases, spreadsheets, schemas) into validated outputs
• Proficiency developing on Linux, with a solid understanding of Git, testing, CI/CD, and monitoring