Tech Stack

Description

You will accelerate manufacturing processes at SpaceX by developing and deploying computer vision models for real-time inspection and defect recognition. As a technical leader, you will guide the team in applying cutting-edge AI to non-destructive evaluation and materials engineering applications. Your work will directly impact the Starship, Raptor, and Starlink programs.

Requirements

• Bachelor's degree in computer science, engineering, math, physics, or related STEM discipline; OR 8+ years of professional experience building and deploying AI software/Machine Learning in lieu of a degree
• 5+ years of software development experience
• 3+ years deploying AI models to production environments
• 3+ years of strong C++ software engineering experience
• 3+ years of experience leveraging Python for data analysis
• Ability to work extended hours and weekends as necessary
• Ability to travel to other SpaceX sites as needed (up to 20%)
• To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State

Responsibilities

• Lead computer vision model development and deployment for real-time inspection and automated defect recognition in production-scale manufacturing
• Architect and implement pipelines for scalable model training, evaluation, deployment, monitoring, and retraining using industry-standard tools like Ray Train/Serve, Kubeflow, Airflow, or equivalent
• Develop software to integrate hardware, sensors, and tooling (including sensor fusion) with AI-driven process monitoring systems
• Build and optimize data pipelines from production lines, incorporating real-time stream processing and inference
• Collaborate with part/process engineers, NDE engineers, and materials scientists to link advanced models and outputs to manufacturing
• Lead design and code reviews, technology evaluations, and enforce best practices (e.g., style, CI/CD, accuracy, testability, efficiency, and standards)