Sr. Software Engineer (Vehicle Engineering)

• Design and train highly reliable, scalable AI/ML models that empower engineers across all SpaceX departments
• Design agentic AI systems and multi-agent workflows to perform engineering tasks
• Build and optimize large-scale machine learning training pipelines to create next-generation AI applications that transform day-to-day engineering operations
• Develop and fine-tune foundation models (LLMs, vision models, multimodal systems) for high-impact SpaceX use cases
• Create production-grade AI tools for data analysis, anomaly detection, predictive modeling, and automated decision-making
• Collaborate with peers on AI architecture, model design, training strategies, and code reviews
• Rapidly build and iterate on AI prototypes, rigorously quantifying model performance, accuracy, and technical constraints
• Own the complete AI model lifecycle — from data preparation and training infrastructure to deployment, monitoring, and continuous improvement
• Deep-dive into complex engineering problems to identify and implement efficient, custom-trained AI solutions
• Establish rigorous AI standards for model validation, safety, reliability, bias mitigation, and data security
• Ensure all AI systems undergo thorough testing and validation to deliver accurate, trustworthy, and production-ready outputs

BASIC QUALIFICATIONS:

• 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
• 5+ years of experience in AI software engineering with a focus on model training, fine-tuning, and machine learning systems (professional and personal projects are applicable)
• 5+ years of programming experience in Python

PREFERRED SKILLS AND EXPERIENCE:

• Strong hands-on experience in AI software engineering with a focus on model training, fine-tuning, and machine learning systems
• Expert understanding of LLM transformer architectures and training procedure including pre-training, supervised fine tuning, and reinforcement learning. - Demonstrated experience training and fine-tuning large language models (LLMs) and other foundation models at scale
• Proven track record training and optimizing machine learning models for computer vision (object detection, segmentation, 3D reconstruction, etc.)
• Deep expertise with modern ML frameworks: PyTorch, TensorFlow, JAX, or equivalent
• Experience designing and running large-scale ML training pipelines, including distributed training on GPU clusters, hyperparameter optimization, and experiment tracking
• Strong understanding of MLOps best practices: model versioning, experiment management (MLflow, Weights & Biases, etc.), CI/CD for ML, and automated retraining
• Strong foundation in statistics, machine learning theory, deep learning architectures, optimization algorithms, and model evaluation
• Proficiency developing on Linux systems
• Solid understanding of version control (Git), testing, continuous integration, deployment, and monitoring for ML systems
• Experience building complexagentic AI systems and multi-agent workflows
• Experience with data infrastructure for training: relational databases (PostgreSQL), non-relational databases, data lakes, and feature stores (vector databases are a plus)
• Experience deploying containerized applications using Docker and Kubernetes

ADDITIONAL REQUIREMENTS:

• Willing to work extended hours and weekends as needed

COMPENSATION AND BENEFITS: