ML Engineer

We’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.

Our 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.

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  Embed 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 them

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  Own 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 sheets

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  Design 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 needed

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  Integrate extracted structure and embeddings into our knowledge store so it gets richer and more valuable with every drawing and every acquisition

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  Build 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 data

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  Collaborate directly with the CTO on technical direction and what we'll build next

• Deep computer vision and VLM experience, ideally on documents, diagrams, or drawings rather than only natural images—detection, segmentation, layout analysis, OCR

• Wants to obsess over this high-leverage data problem: pulling signal out of drawings that were never designed to be parsed by a machine

• Understands embeddings and representation learning—how to build, fine-tune, and evaluate an embedding space, not just call an API

• Ships to production and owns the result; this is an engineering role, not a research-only one

• Has the rigor to be honest about model quality on real data, and to build the evals that keep everyone honest

• Has a deep curiosity for how things work (an organization, a workflow, a market)

• Isn't afraid to expose their ignorance and is constantly asking why

• Has the poise and communication skills to earn trust with people who've never worked with a tech company before

• Is willing to get on a plane with us

• Is 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 yet