Senior AI Engineer, Slingshot Aerospace

• The core competencies we will be looking to identify include intellectual agility, ability to develop innovative solutions, leadership, performance orientation, and industry expertise
• 4+ years Software Engineering, ML Operations, FM Ops, data science deployment, or relevant experience
• Demonstrated experience designing and implementing databases, data systems, feature stores, and modeling pipelines in a data science team environment
• Familiarity with software development lifecycle and CI/CD processes
• Proficiency in Object Oriented Programming
• Ability to work within multi-disciplinary teams in a fast-paced, evolving operational environment that spans military, government, and industry partners
• Passion for Space and AI/ML applications
• Excellent verbal and written communication skills
• BS degree in relevant science or engineering disciplines
• Must be a U.S. citizen eligible for government clearances

Desirable

• In this role, you will join the AI and Data Science department within Slingshot’s Research and Development (R&D) organization
• You will directly impact Slingshot’s vision to accelerate space sustainability to create a safer and more connected world. In your new role you will be responsible for building and maintaining a comprehensive software ecosystem that enables cutting-edge AI research and production deployment
• This includes developing robust experimentation frameworks and MLOps pipelines for both reinforcement learning and large language model workflows, maintaining scalable simulation environments for physics informed training, and implementing production-ready infrastructure
• The team ensures high software engineering standards through automated testing and CI/CD practices, while providing research teams with standardized tools, reusable components, and documentation that accelerate innovation cycles
• Build and maintain standardized experimentation frameworks, data pipelines, and reusable components that accelerate AI research cycles while ensuring reproducibility and comprehensive evaluation across RL and LLM domains
• Design and operate robust MLOps infrastructure with CI/CD pipelines, monitoring systems, and security controls that enable reliable deployment of AI applications at scale
• Develop and maintain scalable simulation environments and integration interfaces the seamlessly connect with AI training and inference pipelines to support diverse scenarios and specialized use cases
• Uphold software engineering excellence through comprehensive testing, code quality standards, documentation, and knowledge sharing
• Collaborate with research and product teams to help identify areas for systemic improvements in the R&D pipeline