AI Engineer III - Blue Ring

Application close date:

Applications will be accepted on an ongoing basis until the requisition is closed.

At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We’re working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight!  

The role is part of the In-Space Systems business unit, which is focused on addressing two of the most compelling challenges in spaceflight today: space infrastructure and increasing mobility on-orbit.

Blue Ring is Blue Origin's multi-mission space mobility platform, and this team is building the next generation of intelligent ground systems that will redefine how satellite fleets are operated—moving beyond manual monitoring and reactive processes.

We are seeking an AI Engineer III to join the Blue Ring software team. You will help develop and deliver the AI and machine learning capabilities embedded in Blue Ring's ground system, spanning multi-agent monitoring, predictive anomaly detection, conversational knowledge retrieval, and AI-assisted decision-making that enable a small operations team to manage an entire satellite fleet. This role requires a hands-on engineer with deep expertise across the full AI/ML development lifecycle, from building multi-agent systems and retrieval-augmented generation pipelines to training and deploying time-series ML models on real spacecraft telemetry.

Role and Responsibilities:

As a key member of the Blue Ring AI/ML engineering team, you will build, deploy, and iterate on AI/ML systems that serve as the intelligence layer for Blue Origin's satellite fleet operations. Working within the architectural direction set by the team lead, you will translate system designs into production-ready implementations and contribute technical recommendations that shape the platform's evolution. You will be expected to:

Multi-Agent System Development:

• Implement and optimize multi-agent systems that coordinate across telemetry analysis, anomaly detection, flight dynamics, and mission planning domains
• Build and maintain agent orchestration frameworks that integrate with LLM services and ground system services for reliable agent execution with observability and memory
• Develop and deploy RAG pipelines over flight manuals, operations procedures, design documents, and historical log files
• Manage vector database infrastructure, embedding pipelines, and retrieval strategies to deliver accurate answers to operator queries
• Implement human-in-the-loop decision workflows where agents present investigation plans and remediation options for operator approval
• Develop confidence scoring and validation systems for AI recommendations
• Deploy containerized AI services on AWS with infrastructure-as-code, CI/CD pipelines, observability, and automated testing
• Support on-call rotations and drive root-cause analysis for production issues

Model Development (Post-training and Time-series Analysis)

• Develop anomaly detection models that identify telemetry deviations beyond threshold-based monitoring
• Train and deploy sequence-based models for failure prediction on telemetry data
• Build automated model retraining pipelines that incorporate operator feedback and new flight data
• Explore compressed models for resource-constrained deployment scenarios

Required Qualifications:

• Able to work onsite in one of our Kent, WA, Renton, WA, or Reston, VA offices.
• Bachelor's or Master's degree in Computer Science, Machine Learning, Electrical Engineering, or a related field
• 5+ years of professional software development experience with a focus on AI/ML applications in production environments
• Proficiency in Python and at least one AI/ML framework (PyTorch, TensorFlow)
• Hands-on experience building and deploying multi-agent systems, agentic AI workflows, or LLM-based applications in production
• Experience designing and operating RAG pipelines, including vector databases, embedding models, and retrieval strategies
• Experience with time-series ML models (RNNs, LSTMs, Transformers) for anomaly detection or forecasting on sensor/telemetry data
• Experience with cloud platforms (AWS preferred), containerization (Docker, Kubernetes), and CI/CD pipeline implementation
• Knowledge of professional software engineering practices including code reviews, source control, automated testing, and operational excellence
• Strong analytical and problem-solving skills with attention to detail
• Excellent written and verbal communication skills f