About the Role
Be a member of the Artificial Intelligence Software Engineering team focusing on solving complex AI data problems for our launch vehicles and spacecrafts.
Our team is creating AI systems to accelerate software development and test, avionics design, flight data review, logistics and mission operations. Your work will directly support the world's largest satellite communication and AI constellations, accelerate rapid reuse of the Falcon launch vehicle, and contribute to the development of the world's largest rocket capable of sending humans to Mars.
The role will consist developing core AI technologies to accelerate engineering. You will be responsible for training internal engineering models on SpaceX data. This will consist of training models from scratch, fine tuning models, and using Reinforcement Learning (RL) to post train LLMs. You will also employ other Machine Learning (ML) techniques to intelligently extract knowledge hidden from SpaceX data. You will also be responsible for working with a diverse set of AI technologies including building Agentic engineering tools, including engineering RAG based tool, and engineering MCP servers. The AI tools and models will used to help engineers rapidly design and test space hardware including electronics and propulsion systems. The team will work closely with engineers throughout the company to create new AI systems to unlock engineering bottlenecks and transform how engineering data is accessed and used at SpaceX.
Aerospace experience is not required to be successful here - rather we look for smart, motivated, collaborative engineers who love solving problems and want to make an impact on an inspiring mission. You will have full ownership of challenging problems and work with a team of enthusiastic engineers to design and produce AI solutions that enable SpaceX to move towards our goals at a rapid pace.
Responsibilities
- 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
- 1+ years of experience in AI software engineering with a focus on model training, fine-tuning, and machine learning systems
- 1+ years of programming experience in Python
Preferred Skills and Experience
- 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 complex agentic 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