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 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.

Cloud & Infrastructure AWS:

Experience with AWS services and architecture. Familiarity with other cloud providers (GCP, Azure) is a plus. S oftware

Engineering Language:

Real-world experience with Python. Secondary proficiency in TypeScript 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 reproducible cloud deployments.

AI & Agentic Systems LLM Fundamentals:

Deep understanding of how large language models work — context windows, structured output, prompt engineering, and model selection trade-offs.

Evaluation:

Ability to design and implement evaluation strategies for complex LLM workflows, measuring correctness, reliability, and performance of multi-step autonomous systems.

Preferred Qualifications Agentic Patterns:

Experience building agents with agentic libraries like Pydantic AI.

Tool Integration:

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. Additional Information Benefits Paying attention to who we are as a company-people, family members, friends, and colleagues-is our primary focus at Element 84. There are lots of ways to run a company, and, for us, we prioritize wanting to come to work, being around people we enjoy, taking on big things with people you trust, and sharing our achievements as a team. You'll get credit when things go right and we'll have your back when things go wrong. We only take on work that is challenging and right for us. There are projects we will turn down and the team has a say. We may be a small company, but we have big company benefits meant to support the idea that we're here for the long term and happiness comes from much more than where you work. We have an extraordinary retention rate because we hire extraordinary people. We hope that's you.

Estimated Range:

The salary for this position is estimated to be $156,500 - $189,000. The final salary offer will be dependent on the candidate's specific experience level, technical proficiency, and alignment with the requirements of the role. Candidates may be below the range to begin and enter into the range after strong performance is demonstrated. This is a full time, salaried position. Please submit your information, resume and cover letter if you are interested. Remote candidates are encouraged, but your home office must be less than 100 miles from either Element 84's Alexandria, VA or Philadelphia, PA hub. 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AI Systems Engineer (Aerospace Integrations) (DC or Philly Metro/Remote)