Software Engineer III - Applied AI

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!

This role is part of Enterprise Technology (ET), where we’re developing the digital infrastructure needed to build the road to space, with an emphasis on digital capabilities required to advance Blue Origin’s mission. Enterprise Technology is the center of excellence for digital technology at Blue Origin, providing oversight and governance to align technology and business strategies.

We are seeking a skilled and self-directed Software Engineer III - Applied AI to join our enterprise search and AI team. You'll take meaningful ownership over the architecture and delivery of agentic, workflow-driven search and discovery platforms - powering enterprise knowledge graphs and AI-assisted retrieval systems that help Blue Origin teams find critical data in seconds.

At this level, you will drive technical decisions, mentor junior engineers, lead cross-functional delivery, and bring rigor to the areas that matter most: retrieval quality, LLM evaluation, frontend engineering depth, and system observability. You will help mature the team's standards in these areas and set the example for how we build production-grade AI applications.

Key Responsibilities

• Lead full-stack development of search, RAG (Retrieval-Augmented Generation), and agentic AI applications using Python, TypeScript/React, and Java
• Own retrieval pipeline quality: design and maintain evaluation harnesses for chunking strategies, embedding models, reranking, and end-to-end RAG response quality; drive improvements through offline and online evals
• Build and maintain LLM evaluation frameworks - including automated regression suites, human preference datasets, and task-specific benchmarks - to ensure consistent, trustworthy AI output
• Architect and deliver production-grade React frontends with deep component design, accessible UI patterns, state management (Redux, Zustand, or equivalent), and performance profiling
• Establish observability standards for AI systems: distributed tracing, structured logging, LLM-specific metrics (latency, token cost, hallucination rate, retrieval relevance), dashboards, and alerting
• Design and operate AWS-based infrastructure (ECS/EKS, Lambda, OpenSearch, S3, CloudWatch) using Infrastructure as Code (Terraform/CDK); champion DevOps practices including CI/CD, automated testing gates, and release management
• Collaborate with other engineers to integrate embedding models, vector databases, and reranking systems into production-ready pipelines