Sr. Software Engineer (Vehicle Engineering)
$160,000–$225,000 year
On-site · Hawthorne, California, United States
Job Summary
Sr. Software Engineer (Vehicle Engineering) to design and train highly reliable AI/ML models, design agentic AI systems and multi-agent workflows, build large-scale training pipelines, develop and fine-tune foundation models (LLMs, vision models, multimodal systems), create production-grade AI tools for data analysis and anomaly detection, collaborate on AI architecture and training strategies, rapidly iterate prototypes while quantifying model performance, and own the complete AI model lifecycle from data prep to deployment and monitoring. Role emphasizes solving complex AI data problems for launch vehicles and spacecraft, enabling SpaceX to accelerate engineering tasks and transform how engineering data is accessed and used.
Required 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
Desired Qualifications
- 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 procedures
- Proven track record training and optimizing machine learning models for computer vision (object detection, segmentation, 3D reconstruction, etc.)
- Experience deploying containerized applications using Docker and Kubernetes
- Experience with data infrastructure for training (PostgreSQL, non-relational databases, data lakes, feature stores, vector databases)
- Proficiency developing on Linux systems
- Solid understanding of version control (Git), testing, CI/CD for ML, and monitoring for ML systems
- Experience designing and running large-scale ML training pipelines with distributed training on GPU clusters
- Strong understanding of MLOps best practices: model versioning, experiment management (MLflow, Weights & Biases), CI/CD for ML
- Experience with agentic AI systems and multi-agent workflows
- Experience with reinforcement learning and foundation models
- Ability to own the complete AI model lifecycle from data prep to deployment and monitoring
- Experience with AI tooling for data analysis, anomaly detection, predictive modeling and automated decision-making
- Ability to work with cross-functional teams to produce AI solutions for space hardware design and testing
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