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.

• https://www.eagleeng.com/

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