About this role
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.
AI SOFTWARE ENGINEER (VEHICLE ENGINEERING)
SpaceX is building AI systems that accelerate engineering and make internal operations more effective. We are looking for software engineers who combine strong programming skills with practical machine learning knowledge and a willingness to solve unfamiliar problems.
You will develop, evaluate, and deploy AI capabilities across a range of engineering and business needs. Projects may include building tools and training environments for agents, developing machine learning systems with limited real-world data, and delivering internal AI applications. The work will evolve as new capabilities and opportunities emerge.
This role combines experimentation, software development, and secure infrastructure. You will work with engineers, domain experts, and business partners to take ideas from initial prototypes through deployment, using rigorous evaluations to understand performance, identify failures, and guide improvements.
Candidates may bring depth in different areas. We value strong fundamentals, careful experimentation, demonstrated contributions, and the ability to grow across the work described. Prior aerospace experience is not required.
RESPONSIBILITIES:
• Build AI systems and applications that address engineering and business problems, working with users to understand requirements and define measurable success criteria.
• Develop evaluations (evals), benchmarks, and representative datasets to measure model and agent performance, compare approaches, and detect regressions.
• Build automated graders and scoring methods, validate their agreement with expert judgments, and investigate misleading results, data leakage, and other evaluation failures.
• Create reproducible environments for training and evaluating agents, including task definitions, tool interfaces, constraints, and feedback signals.
• Integrate models and agents with engineering tools, internal services, and company data, capturing execution traces and results to support debugging and evaluation.
• Establish baselines, run experiments, and analyze failures to determine whether changes to data, models, tools, or workflows produce meaningful improvements.
• Develop data preparation, training, and evaluation workflows for problems with limited real-world data, including targeted data collection, labeling, augmentation, and synthetic data where appropriate.
• Use Docker and Kubernetes to build and operate reliable workloads with appropriate isolation, access controls, resource management, logging, and reproducibility.
• Collaborate with engineering, infrastructure, security, HR, and legal partners to implement requirements for data handling, traceab