AI Systems Engineer (Aerospace Integrations) (Hub-Remote: DC or Philly Metro)
About The Position
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 won't just be "calling an API"; you will be architecting and building autonomous agentic workflows that orchestrate heterogeneous AI techniques (LLMs, reinforcement learning, RAG, uncertainty quantification), building integrations with engineering tools (CAD, FEA, CAM), and deploying scalable infrastructure on AWS. Your goal is to turn science objectives into manufacturable hardware designs through reliable, AI-driven automation. As our AI Domain Expert, you will bridge the gap between generative AI and physical reality. You will be responsible for ensuring that our text-to-spaceship pipeline generates physically viable, safe, and manufacturable spacecraft components. You will translate complex physics, NASA standards, and aerospace load cases into programmatic guardrails for our multi-agent systems, ensuring our AI generates structurally sound flight hardware.
Requirements
- Mechanical & Aerospace Engineering Fundamentals
- 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.
- Hands-on experience with the scripting APIs of industry-standard engineering tools (e.g., Python APIs for Autodesk Fusion 360, ANSYS, NASTRAN, or SolidWorks).
- Familiarity with aerospace-grade materials (titanium, aluminum alloys, carbon fiber composites) and how their properties dictate design limits.
- Ability to interpret and programmatically apply GD&T (Geometric Dimensioning and Tolerancing) and NASA/aerospace engineering standards.
- Experience with AWS services and architecture. Familiarity with other cloud providers (GCP, Azure) is a plus.
- Real-world experience with Python. Secondary proficiency in TypeScript is a plus.
- Strong grasp of API design, containerization, and connecting heterogeneous tools and data formats into automated pipelines.
- Experience with IaC tools and reproducible cloud deployments.
- Deep understanding of how large language models work — context windows, structured output, prompt engineering, and model selection trade-offs.
- Ability to design and implement evaluation strategies for complex LLM workflows, measuring correctness, reliability, and performance of multi-step autonomous systems.
Nice To Haves
- Experience building agents with agentic libraries like Pydantic AI.
- Expertise in the Model Context Protocol (MCP) or equivalent approaches for connecting AI agents to external APIs, databases, and domain-specific tools.
- Experience with ML techniques beyond LLMs (e.g., reinforcement learning, uncertainty quantification, reduced-order models) applied to design optimization or engineering problems.
- Contributions to open-source AI libraries or a portfolio of deployed LLM applications.
Responsibilities
- Translate physical constraints (mass, thermal envelopes, radiation shielding, vibro-acoustics) and mechanical properties into prompt contexts, structured outputs, and agent guardrails.
- Design and implement programmatic workflows for Finite Element Analysis (FEA) and computational fluid dynamics (CFD) that the AI agents can autonomously trigger to validate their own designs.
- 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.
- 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.
- Build pipelines that convert natural language mission requirements into structured specifications (text → JSON) and implement RAG pipelines for engineering knowledge retrieval.
- Connect AI agents to external engineering tools (CAD, FEA, CAM software) via MCP and custom API integrations, enabling agents to drive design, analysis, and manufacturing workflows.
- Deploy and scale AI workloads across cloud providers (AWS, GCP, Azure) using containerized architectures. Apply cloud security best practices for government data.
- Build evaluation frameworks for agentic applications — measuring agent performance, design quality, and pipeline reliability across multi-step autonomous workflows.
Benefits
- Competitive medical, dental and vision benefits
- Life Insurance, Short & Long Term disability insurance
- Voluntary Accident, Critical Illness & Hospital Insurance
- 401(k) and Roth 401(k) retirement plans with a fixed 3% of salary employer contributions (paid regardless of employee participation)
- Health savings account with a company contribution
- Flexible spending accounts (medical, dependent care and transportation)
- Company-paid parental leave after one year of employment
- Flexible work schedules
- Paid employee assistance program
- 6 paid floating holidays
- 4 weeks + 1 day paid Vacation Time Off per calendar year (prorated first year)
- 40 hours paid Sick Leave
- Cell phone stipend
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
