Forward Deployed Engineer

Higharc•New York, NY

53d•Remote

About The Position

Higharc is a VC-backed startup that is changing how new homes are designed and built. Join a founding team who’ve shipped products for Autodesk, Electronic Arts, Nike, and Apple. We have raised over $175M with support from top-notch venture capital firms and more than 18 strategic investors: industry leaders in construction, building products manufacturing, and distribution. Higharc is seeking a Forward Deployed Engineer to join as one of the founding roles on our new Forward Deployed team. You'll work embedded alongside a large production homebuilder to turn plans into actionable data. A homebuilder's plan library is the product data behind its entire business — and at enterprise scale, that data is almost entirely unstructured. Plans live as drawing sets scattered across semi-autonomous divisions, renamed and re-numbered as they move. Nobody can say with confidence which plans are duplicates, how many versions of a given home exist, or what actually varies between them. Higharc converts those catalogs into structured, queryable plan data, and builds the analysis on top of it: searchable plan libraries with agent-enabled natural-language search, geometric similarity scoring that surfaces plans which have forked under different names, structured mapping of what varies across a library, and classification that makes consolidation decisions possible. You'll build what gets shipped in the field. This role is for someone who wants to build against real, messy data in front of the customer, not two abstractions removed from it.

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, and a habit of interrogating a data set yourself rather than requesting a pull
Experience prototyping to validate ideas quickly using tools like Cursor, Replit, or V0.dev
A track record building structured data models, taxonomies, or classification schemes and getting a system to actually run on them in production
Strong judgment about data quality — what level of accuracy a decision requires, and what it costs to get there
Excellent written and verbal communication, including with executive stakeholders who have limited time and no patience for status without substance
Willingness to travel to client sites and company events up to 30% of the time

Nice To Haves

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 working 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

Responsibilities

Embed with HQ, regional, and division teams to run technical discovery — profile plan assets, assess data quality, and 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 the data — write the ETL, build against internal APIs, and ship working capability back to users directly rather than handing off a spec
Build geometric similarity scoring and classification models that surface plans which have forked under different names
Meet delivery milestones — coverage targets, data-field completeness, QA error thresholds — and flag tradeoffs in real time when schedule pressure argues against them
Instrument and quantify results: redundancy across a catalog, dimensional spread on repeated conditions, consolidation candidates, and the purchasing leverage each one represents
Partner with the Forward Deployed Product Manager, Technical Lead, QA, Intake Specialists, and Client Strategy, and present technical progress and tradeoffs to executive stakeholders
Build the repeatable version — turn one-off scripts and data patterns into reusable tooling that lets the forward deployed model work on the next engagement, not just the current one