Eagle
ML Engineer
New York City
Sponsorship not specified$150k-$300kDetected 41 days ago
RESTMachine LearningComputer VisionProduct StrategyResearchCommunication
About the role
- Our core thesis: 85% of what engineers do today is theoretically automatable, yet less than 5% has actually been touched by AI.
- 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.
Responsibilities
- 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
- 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
- Collaborate directly with the CTO on technical direction and what we'll build next
- Wants to obsess over this high-leverage data problem: pulling signal out of drawings that were never designed to be parsed by a machine
- Ships to production and owns the result
- Has the rigor to be honest about model quality on real data, and to build the evals that keep everyone honest
Skills
- 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.
Compensation
- Competitive cash compensation ($150K-$300K depending on experience)
- In-person office in NYC
Benefits
- Deep computer vision and VLM experience, ideally on documents, diagrams, or drawings rather than only natural images-detection, segmentation, layout analysis, OCR
- 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
- Understands embeddings and representation learning-how to build, fine-tune, and evaluate an embedding space, not just call an API
- Founding equity, scaled to scope
- Full healthcare benefits
Company info
- 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.
This listing is sourced directly from Eagle's careers page and normalized into a canonical job model.