Application Guide

How to Apply for AI Systems Engineer (Aerospace Integrations) (Hub-Remote: DC or Philly Metro)

at Element84

🏢 About Element84

Element84 is a mission-driven tech company that builds critical software for NASA, NOAA, and other federal agencies. They specialize in cloud-native geospatial and AI systems, with a culture that values open source, collaboration, and real-world impact. Working here means contributing to space exploration and national infrastructure.

About This Role

You'll design multi-agent AI systems that translate natural language into validated spacecraft designs—essentially building the 'AI engineer' for NASA's next-generation missions. This role combines cutting-edge AI (LLMs, RL, RAG) with aerospace engineering, directly enabling faster, safer spacecraft development.

💡 A Day in the Life

You'll start by reviewing mission requirements from NASA stakeholders, then design and test agentic workflows that generate CAD models and run FEA simulations. Afternoons might involve debugging AWS infrastructure, collaborating with aerospace engineers to validate outputs, and iterating on guardrails to ensure safety and compliance.

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Interview Prep

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🎯 Who Element84 Is Looking For

  • Has 2+ years of software engineering in aerospace/defense, with hands-on experience integrating APIs of CAD/CAE tools like Fusion 360, ANSYS, or SolidWorks.
  • Deep Python expertise, with secondary TypeScript proficiency; strong in API design, containerization (Docker), and AWS cloud infrastructure.
  • Understands aerospace hardware lifecycle, GD&T, and materials—able to translate engineering standards into programmatic rules for AI guardrails.
  • Comfortable with uncertainty quantification, reinforcement learning, and RAG; experience building autonomous agentic workflows is a plus.

📝 Tips for Applying to Element84

1

Highlight specific projects where you integrated AI with engineering tools (e.g., automating CAD generation or simulation workflows).

2

Showcase any experience with NASA or aerospace standards (e.g., NASA-STD-8719.9, ASME Y14.5) and how you've coded them into validation logic.

3

Emphasize your AWS skills—mention specific services (SageMaker, Lambda, Step Functions) and how you've deployed scalable ML pipelines.

4

Include a link to a GitHub repo or project demonstrating multi-agent systems, LLM orchestration, or RAG pipelines.

5

Tailor your resume to mention 'Text-to-Spaceship' or similar natural language to design systems; show you understand the domain.

✉️ What to Emphasize in Your Cover Letter

['Your passion for aerospace and AI—explain why you want to build systems that turn mission requirements into spacecraft components.', 'Specific examples of integrating LLMs or RL with engineering tools (e.g., using GPT-4 to generate CAD parameters and validating with FEA).', 'Your experience with safety-critical systems and how you ensure AI outputs are physically viable and manufacturable.', "Familiarity with Element84's open-source contributions or NASA projects; mention any work with STAC, COGs, or similar geospatial tools."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read about Element84's work on NASA's VEDA (Visualization, Exploration, and Data Analysis) platform and their open-source geospatial tools.
  • → Familiarize yourself with NASA's 'Text-to-Spaceship' initiative or similar AI-for-design programs (e.g., ESA's AI for spacecraft).
  • → Review the basics of GD&T (ASME Y14.5) and common aerospace materials (Al 7075, Ti-6Al-4V) to speak knowledgeably about manufacturability.
  • → Look into Element84's company culture—they emphasize remote collaboration, so check their blog or engineering talks for insights.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a multi-agent system for converting a natural language mission requirement (e.g., 'low Earth orbit imaging satellite') into a component list—walk through architecture, tools, and guardrails.
2 How would you integrate uncertainty quantification into a reinforcement learning agent for spacecraft design? Discuss trade-offs.
3 Explain how you'd containerize a CAD API integration pipeline and deploy it on AWS with auto-scaling and monitoring.
4 Given a set of aerospace standards (e.g., margin of safety requirements), how would you translate them into programmatic rules for an LLM-based agent?
5 Describe a time you had to debug an AI model that produced physically impossible designs. What steps did you take?
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⚠️ Common Mistakes to Avoid

  • Don't focus solely on AI without showing aerospace/engineering domain knowledge—this role requires both.
  • Avoid generic AI buzzwords; be specific about tools (e.g., 'LangChain for agent orchestration' vs. 'LLMs').
  • Don't neglect the 'integrations' part—failing to mention CAD/CAE API experience or containerization can sink your application.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Element84!

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