Applied AI Engineer

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 https://www.linkedin.com/in/mayank-mardia/ (Penn, ex-Bloomberg, ex-Ethic) and Sohum https://www.linkedin.com/in/sohum-daftary-779999100/ (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