Applied AI Engineer
About Eagle
Eagle is an AI native platform on a mission to unleash engineering services for the built environment.
We acquire and scale world-class engineering firms by putting proprietary technology into the hands of the engineers who design our nation’s critical infrastructure (think water systems, power utilities, bridges, and more).
Our ambition is to create the first AI-native engineering design firm to meet the country's generational energy, climate, and infrastructure needs.
We're backed by Lightspeed Venture Partners, a global venture capital firm.
Eagle was founded by Mayank (Penn, ex-Bloomberg, ex-Ethic) and Sohum (Penn M&T, ex-Long Ridge)
The Role
Eagle is seeking an Applied AI Engineer to build systems that automate the most time-consuming parts of civil engineering. Our goal is unleash civil, structural, and MEP engineers to focus on design, not documentation.
You'll work alongside civil engineers daily, translating real-world infrastructure challenges into production AI solutions for water, transportation, and utility projects.
What You'll Do
Collaborate directly with civil engineers to understand domain-specific workflows and translate them into AI-powered tools
Build AI systems that automate drafting, calculations, and documentation — freeing engineers to focus on higher-value design work
Develop spatial reasoning models that can interpret blueprints and generate preliminary designs for standard infrastructure projects
Create agents that synthesize regulatory standards, run modeling simulations, and automate repetitive engineering tasks
Integrate LLMs and multimodal models into production workflows that serve real engineering teams
Own the full lifecycle — from prototyping and experimentation to deployment and iteration
What You'll Bring
5+ years of experience in software engineering, with 2+ years focused on applied AI/ML
Hands-on experience building and deploying LLM-based applications in production
Strong Python skills and familiarity with ML frameworks (PyTorch, HuggingFace, etc.)
Experience with RAG architectures, prompt engineering, and/or fine-tuning
Comfort with ambiguity—you can take a fuzzy problem and figure out what to build
Bonus: Experience with computer vision, CAD file formats, or spatial data
Bonus: Background in engineering, AEC, or other technical domains