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# AI Software Engineer (Vehicle Engineering)

- **Company:** Space Exploration Technologies Corp.
- **Location:** Los Angeles County, CA, United States
- **Salary:** $125,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Testing (Software), Application Programming Interfaces (APIs), Artificial Intelligence, Computer Programming, Databases, Data Cleansing, Information Leak Prevention, Software Debugging, Linux, Python (Programming Language), Machine Learning, Software Tools, Software Engineering, Management of Software Versions, Reinforcement Learning, Data Logging, Data Processing, Pytorch, Backend, Kubernetes, Information Technology, Machine Learning Operations, Software Version Control, Docker, Data Generation
- **Published:** October 27, 2026
- **Apply:** https://www.dice.com/job-detail/71c79878-44d7-41c7-aedd-84ad19b1b32b

## About the Role

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., 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., * Bachelor's degree in computer science, data science, engineering, math, or physics; OR 4+ years of professional experience building and training AI/ML systems in lieu of a degree

 * +1 years of Python programming development and debugging software on Linux.
 * +1 years of experience with ML framework such as PyTorch, or practical experience developing agents and model-based applications.
 * +1 years of experience building or evaluating AI/ML systems through professional work, internships, research, or substantial independent projects.

PREFERRED SKILLS AND EXPERIENCE:

 * Understanding of evaluation fundamentals, including appropriate metrics, baselines, separation of training and evaluation data, error analysis, and reproducible experimentation.
 * Experience with Docker and familiarity with Kubernetes.
 * Familiarity with version control, software testing, and collaborative development practices.
 * Ability to explain technical decisions, work with people across disciplines, and learn unfamiliar tools and problem domains.
 * Designing task-specific evals, evaluation datasets, automated graders, or frameworks for comparing model and agent performance.
 * Building reinforcement learning environments, agent tools, reward functions, or verifiers.
 * Working with model fine-tuning, reinforcement learning, or learning from human feedback.
 * Developing effective ML systems with limited data, including data curation, augmentation, and synthetic data generation.
 * Deploying and troubleshooting Kubernetes workloads and distributed job execution.
 * Building systems for experiment tracking and versioning datasets, models, environments, and evaluations.
 * Developing backend services, APIs, databases, and interfaces for reviewing AI outputs.
 * Implementing systems involving sensitive data, audit trails, or structured human oversight.
 * Taking a technical project from initial requirements through implementation, evaluation, and deployment.

## Description

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., * 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, traceable outputs, auditability, and human review.
 * Maintain deployed systems through testing, monitoring, ongoing evaluation, and operational debugging, improving quality, reliability, latency, and cost based on evidence and user feedback.