ML Engineer

ML Engineer LocationNew York CityEmployment TypeFull timeLocation TypeOn-siteDepartmentEngineeringAbout EagleWe’re on a mission to radically transform the way we design and construct our built environment.Backed by Lightspeed Venture Partners, Eagle acquires and transforms civil, structural, and MEP engineering firms with applied AI. We’re an AI laboratory dedicated to providing engineers with the tools they need to solve the world’s hardest infrastructure, energy, and climate problems.By arming designers with frontier technology, our ambition is to build the most valuable, talent-dense engineering firm in the United States.The opportunityOur core thesis: 85% of what engineers do today is theoretically automatable, yet less than 5% has actually been touched by AI. That gap is the largest of any profession. Our plan is to close it by acquiring engineering firms, building purpose-built tools for their staff, and compounding that proprietary intelligence across acquisitions.The richest, most defensible data in this industry lives in 2D drawings—drawings sets, details, sections, schedules—and only a small fraction of it is machine-readable today. As a Machine Learning Engineer, you'll own the problem of turning that visual information into structured, embedded, queryable intelligence. You'll work directly with the CTO, and the work you do becomes the foundation the rest of the platform compounds on top of. You get a front-row seat to building a company from zero—engaging with architecture decisions, firm acquisitions, and product strategy—on a problem domain that's barely been touched by AI.What you'll doEmbed with staff at engineering firms alongside the founders; get your hands on real drawing sets and learn how engineers actually read, mark up, and reuse themOwn the drawing-parsing pipeline end-to-end—ingestion of PDF and CAD exports, layout analysis, symbol and entity detection, OCR on dimensions and notes, and extraction of schedules and title-block metadata from noisy, inconsistent real-world sheetsDesign the embedding strategy for drawings: how to represent a sheet, a detail, or a region as a vector so it can be searched, compared, and reasoned over—adapting or fine-tuning vision and multimodal encoders as neededIntegrate extracted structure and embeddings into our knowledge store so it gets richer and more valuable with every drawing and every acquisitionBuild the evaluation harness this all depends on—ground-truth sets, accuracy metrics, and a tight loop for measuring whether the models actually work on messy production dataCollaborate directly with the CTO on technical direction and what we'll build nextWhat we look forDeep computer vision and VLM experience, ideally on documents, diagrams, or drawings rather than only natural images—detection, segmentation, layout analysis, OCRWants to obsess over this high-leverage data problem: pulling signal out of drawings that were never designed to be parsed by a machineUnderstands embeddings and representation learning—how to build, fine-tune, and evaluate an embedding space, not just call an APIShips to production and owns the result; this is an engineering role, not a research-only oneHas the rigor to be honest about model quality on real data, and to build the evals that keep everyone honestHas a deep curiosity for how things work (an organization, a workflow, a market)Isn't afraid to expose their ignorance and is constantly asking whyHas the poise and communication skills to earn trust with people who've never worked with a tech company beforeIs willing to get on a plane with usIs not above any task: up to label the data yourself, write the annotation tooling, or hand-tune a heuristic when the model isn't ready yetCompensationCompetitive cash compensation ($150K–$300K depending on experience)Founding equity, scaled to scopeFull healthcare benefitsIn-person office in NYCApply for this Job