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

About this role

ABOUT ELEMENT 84

Element 84 is a woman-owned small business that works with public, private, and non-profit sector clients to develop geospatial data processing pipelines & build software that helps answer big questions about our health, our infrastructure, and our changing planet. We solve challenging problems across a wide range of industries, but our super power is cloud-based geospatial data processing, remote sensing, and Earth science systems. Our headquarters is located in Alexandria, VA and we have a satellite office in Philadelphia, PA. Element 84 also supports a large remote workforce from a variety of other states.

A FEW OTHER THINGS YOU SHOULD KNOW

1. Element 84's values are: We Invest in Each Other, We are Reliable, and Our Work Benefits our World.

2. Element 84 works on meaningful projects that are challenging—from a technical, people, and team perspective. We make an impact on large projects through our leadership and expertise, both formal and informal.

3. Element 84 values life outside of work. We offer parental leave for everyone and support each other through family challenges like medical problems, adoptions, and new babies.

4. Element 84 has had a flexible work schedule since our founding in 2010.

5. Element 84 is committed to fostering a culture of curiosity and respect for all individuals as we constantly strive to create a work environment where everyone feels welcome and supported.

ABOUT YOU

You are curious about the world, are constantly learning, driven to lead (formally or informally), 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.

ROLE OVERVIEW

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.

KEY RESPONSIBILITIES

- 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 Finite Element Analysis (FEA) and computational fluid dynamics (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 software) 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.

TECHNICAL REQUIREMENTS

1. 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 SolidWorks).

- 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.

2. Cloud & Infrastructure

- AWS: Experience with AWS services and architecture. Familiarity with other cloud providers (GCP, Azure)…