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Higharc is a VC-backed startup transforming how new homes are designed and built. The company has raised over $175M and counts top venture capital firms and 18+ strategic investors from construction, building products, and distribution sectors among its backers. The founding team has shipped products for Autodesk, Electronic Arts, Nike, and Apple.
You'll join as a Forward Deployed Engineer on a founding team, embedded with large production homebuilders to convert unstructured plan data into actionable intelligence. Homebuilders' plan libraries—the product data behind their entire business—exist as scattered drawing sets across semi-autonomous divisions, often renamed and re-numbered as they move. Nobody can confidently identify duplicates, count versions, or articulate what varies between plans.
Higharc converts these catalogs into structured, queryable data and builds analysis on top: searchable plan libraries with agent-enabled natural-language search, geometric similarity scoring to surface plans that forked under different names, structured mapping of variation across libraries, and classification enabling consolidation decisions.
You will:
- Embed with HQ, regional, and division teams to run technical discovery—profile plan assets, assess data quality, reverse-engineer how each division's drawing sets encode variation
- Build and ship the searchable plan library, analytics dashboard, and agent-enabled natural-language search; own the pipeline from raw plan ingestion through structured, queryable output
- Tune and extend Higharc's Autotranslate AI capabilities to identify, categorize, and translate floorplans into data
- Work hands-on with data—write ETL, build against internal APIs, ship working capability directly to users rather than handing off specs
- Build geometric similarity scoring and classification models surfacing plans that forked under different names
- Meet delivery milestones (coverage targets, data-field completeness, QA error thresholds) and flag tradeoffs in real time when schedule pressure conflicts
- Instrument and quantify results: redundancy across catalogs, dimensional spread on repeated conditions, consolidation candidates, and purchasing leverage each represents
- Partner with Forward Deployed Product Manager, Technical Lead, QA, Intake Specialists, and Client Strategy; present technical progress and tradeoffs to executive stakeholders
- Build the repeatable version—turn one-off scripts and data patterns into reusable tooling for future engagements
- Travel to client sites and company events up to 30% of the time.
You're happiest closest to the customer and have learned that the fastest path to the right answer is usually to build it and see. You're comfortable with 70%-specified scope and unstructured datasets. You can review how a plan set is drawn in the morning and ship the validating pipeline by afternoon. You know the difference between a demo and a deliverable.
REQUIREMENTS:
- 5+ years of engineering experience, including significant time in customer-facing or deployed settings (forward deployed engineering, solutions engineering, technical consulting, or professional services)
- Strong hands-on data fluency: SQL, scripting against APIs, comfort in notebooks, habit of interrogating datasets yourself rather than requesting pulls
- Experience prototyping to validate ideas quickly using tools like Cursor, Replit, or V0.dev
- Track record building structured data models, taxonomies, or classification schemes and getting systems to run on them in production
- Strong judgment about data quality—understanding what accuracy level decisions require and what it costs to achieve
- Excellent written and verbal communication, including with executive stakeholders with limited time and no patience for status without substance
- Willingness to travel to client sites and company events up to 30% of the time
MAJOR PLUSES:
- Background in residential construction, homebuilding, architecture, or AEC technology—especially fluency with plan sets, elevations, options, and how builders manage plan variation
- Experience with agent-based or natural-language search over structured data, RAG systems, or LLM-powered analytics
- Exposure to geometric or spatial data—CAD/BIM, computer vision on drawings, similarity and clustering methods
- Experience with large, messy, enterprise-scale data migrations or ingestion programs
- Having built a function from zero: first hire into a new team, new practice, or new delivery model
