AI Systems Engineer; Aerospace Integrations; Hub-Remote: DC or Philly Metro

About You

You are curious about the world, are constantly learning, driven to lead, and have a strong work ethic. You’re interested in solving impactful problems in science, medicine, and other projects that have a societal good. You can work independently or with a team, prioritize your projects, and be effective without micromanagement.

You'll care about writing. Our team is remote and written communication is essential. In addition to caring about a well-crafted email and a succinct conference abstract, you understand that good writing is good design and engineering.

As an AI Systems Engineer, you will design and build the multi-agent systems that power NASA’s Text-to-Spaceship initiative, an AI pipeline that converts natural language mission requirements into validated spacecraft component designs. You’ll architect and build autonomous agentic workflows that orchestrate heterogeneous AI techniques (LLMs, reinforcement learning, RAG, uncertainty quantification), build integrations with engineering tools (CAD, FEA, CAM), and deploy scalable infrastructure on AWS.

Your goal is to turn science objectives into manufacturable hardware designs through reliable, AI-driven automation.

• Physics-Informed AI Constraints:
  Translate physical constraints (mass, thermal envelopes, radiation shielding, vibro-acoustics) and mechanical properties into prompt contexts, structured outputs, and agent guardrails.
• Automated CAE & FEA Workflows:
  Design and implement programmatic workflows for FEA and CFD that the AI agents can autonomously trigger to validate their own designs.
• Domain Validation & Ground Truth:
  Serve as the human-in-the-loop expert to vet AI-generated structural designs, identifying edge cases, hallucinated physics, or impractical geometries, and using those insights to improve the agentic evaluation framework.
• Multi-Agent Architecture:
  Design and implement agentic frameworks where a lead orchestrator agent coordinates specialized agents (optical design, structural, harnessing, analysis, reporting) across complex, multi-step engineering workflows.
• Natural Language to Engineering Output:
  Build pipelines that convert natural language mission requirements into structured specifications (text → JSON) and implement RAG pipelines for engineering knowledge retrieval.
• Tool Integration:
  Connect AI agents to external engineering tools (CAD, FEA, CAM) via MCP and custom API integrations, enabling agents to drive design, analysis, and manufacturing workflows.
• Cloud Infrastructure:
  Deploy and scale AI workloads across cloud providers (AWS, GCP, Azure) using containerized architectures. Apply cloud security best practices for government data.
• Evaluation & Observability:
  Build evaluation frameworks for agentic applications — measuring agent performance, design quality, and pipeline reliability across multi-step autonomous workflows.

• Mechanical & Aerospace Engineering Fundamentals
  • Aerospace or Manufacturing
    
    Experience:
    
    2+ years working as a software engineer within the aerospace, defense, space, or similar manufacturing sectors, with a strong understanding of the hardware engineering lifecycle and launch and spaceflight environment.
  • CAD/CAE Automation:
    Hands‑on experience with the scripting APIs of industry‑standard engineering tools (e.g., Python APIs for Autodesk Fusion 360, ANSYS, NASTRAN, or Solid Works).
  • Materials Science:
    Familiarity with aerospace‑grade materials (titanium, aluminum alloys, carbon fiber composites) and how their properties dictate design limits.
  • Engineering Standards:
    Ability to interpret and programmatically apply GD&T (Geometric Dimensioning and Tolerancing) and NASA/aerospace engineering standards.
• Cloud & Infrastructure
  • AWS:
    Experience with AWS services and architecture. Familiarity with other cloud providers (GCP, Azure) is a plus.
• Software Engineering
  • Language:
    Real‑world experience with Python. Secondary proficiency in Type Script is a plus.
  • API & Systems Design:
    Strong grasp of API design, containerization, and connecting heterogeneous tools and data formats into automated pipelines.
  • Infrastructure as Code:
    Experience with IaC tools and…