Job Openings Guthrie AI — Founding AI/ML Engineer

About the job Guthrie AI — Founding AI/ML Engineer

Guthrie AI — Founding AI/ML Engineer

Type: Full-time | Hybrid (3 days/week onsite) | Washington, D.C. Compensation: $190,000–$200,000 + competitive equity Hiring count: 1 Visa sponsorship: Yes — H-1B Reports to: Chief AI Officer

About Guthrie AI

Guthrie AI is building the AI-powered workflow layer for construction — one of the world's largest industries and one that skipped the software era entirely. Subcontractors, their core customer, have seen no meaningful software innovation in 15+ years, and agents plus multimodal AI are the first real wedge in. Off-the-shelf models from OpenAI and Anthropic struggle with construction diagrams because those aren't in standard training data, which makes training in-house models a core technical bet.

Founded: 2023 | Team size: ~11–50 (Seed stage; Chief AI Officer + 7 engineers — 2 AI/ML, 5 full-stack) | Total funding: Seed round just closed (term sheets signed, not yet announced); ~$2M ARR with a plan to 10x in 12 months Industry: Property Tech / Construction Tech / Agentic AI Website: guthrieai.com Office: Philadelphia, PA (newly opened hub; remote-first team)

Why Candidates Should Join

  • Own the model layer end-to-end: This is the founding AI/ML hire owning the entire model-development side of the platform and setting technical direction for the AI/ML team.
  • A genuinely hard, defensible problem: Construction diagrams and floor plans aren't in any foundation model's training data, so you train in-house models — real CV depth, not API wrapping.
  • Early-stage upside: Meaningful early-employee grant (ESOP pool), fresh seed round, ~$2M ARR with a credible 10x plan over the next 12 months.

Intake Call Summary

  • No intake call transcript was included on the role page. An intake video is present but not transcribed — update this section once the summary is available.

The Role

Guthrie is hiring a Founding AI/ML Engineer with deep computer vision expertise to own the model-development side of the platform and lead the in-house model work that off-the-shelf foundation models can't handle.

What You'll Be Doing

  • Train and improve in-house computer vision models for construction diagrams (segmentation, object detection, classifiers)
  • Lead architectural and model-improvement work where off-the-shelf models hit their ceiling
  • Own the model-development roadmap and set technical direction for the AI/ML team
  • Build agentic orchestration and retrieval flows (RAG, fine-tuning, local vs foundation model decisions, cost optimization)
  • Provide technical leadership and guidance to the 2–3 existing AI/ML engineers (who will report to this hire)

Tech stack: Python, PyTorch, CUDA; CV (2D segmentation, object detection, classifiers; SAM / SAM3-tier foundation models); agentic orchestration & retrieval (LLMs, RAG, fine-tuning small language models, local vs foundation model trade-offs)

Role split: ~half computer vision (the irreplaceable part), ~half agentic orchestration/retrieval (where "good enough" is acceptable if CV expertise is exceptional).

Requirements

  • Computer vision, 2D segmentation depth
  • Python + PyTorch + CUDA
  • Train in-house models (not just fine-tune)
  • Construction diagrams / floor plans bonus
  • East Coast, 3 days hybrid at Philly hub

Green Flags

  • 2D segmentation expertise with published work in floor plan localization, raster-to-sequence, structural priors, or comparable architectural CV. Direct domain match.
  • Construction diagrams, floor plans, or technical drawings personal or professional exposure. Genuinely rare and high-signal.
  • PyTorch + CUDA fluency with architectural model improvement track record (not just fine-tuning APIs)
  • Research-trained but now hands-on.
  • Agentic orchestration / retrieval experience as a secondary skill. Strong plus but not blocker.

Red Flags

  • TensorFlow-only background without PyTorch transition. PyTorch is the expected core framework; TensorFlow is dated for this work.
  • Pure backend ML engineer without CV depth.
  • Off-the-shelf model orchestrator without ability to train custom models.
  • Pure research without willingness to ship. The role is hands-on technical leadership, not academic.
  • Cannot do East Coast hybrid (3 days/week onsite). Flex only for genuinely exceptional candidates.

Role Details

Salary$190,000–$200,000EquityCompetitive — meaningful early-employee grant (ESOP pool); no % specifiedExperience6+ yearsOn-site policyEast Coast, 3 days/week onsite at Philly hub (flex for exceptional candidates)Visa sponsorshipH-1BEmployment typeFull-timeLocationWashington, D.C.

Screening Questions

  • None present on the role page — add here if Guthrie provides any.

Interview Process

Stage 1 — Initial Screen (30 min) — 30-minute hiring-manager screen. Stage 2 — Getting-To-Know-You Round — With Tuneer (Head of Product / co-founder), the Product team, and the CEO. Cultural and team fit. Stage 3 — ~2-hour Technical / Architecture Round — With Kaz and one of the other lead engineers. Architecture deep-dive and technical assessment. Stage 4 — Optional On-Site — At the Philly office (Amtrak-friendly from NY / DC). Not required, but a plus. Stage 5 — Offer Extended Stage 6 — Candidate Hired — Candidate accepts and starts.

(Contrario's "Pending Approval" stage precedes Stage 1 as the platform approval gate.)

Ideal Companies & Backgrounds

  • No "Ideal Companies" section was present on the role page.

Ideal Candidate Profiles

For reference only — do not source these specific profiles. The role page labels this section "Ideal Candidates — DO NOT CONTACT."

Hao Phung — LinkedIn link present on the page but the URL was not extractable from the HTML.

  • Computer vision experience in floorplan reconstruction.

The remaining four entries are reference research papers, not candidate profiles: "Waffle Research Paper," "Technical Drawing Research Paper," "Computer Vision Abstraction Research Paper," "3D Diffusion Research Paper" (each links to a paper/site, not a person).

Rejected Candidate Feedback

  • None present on the role page.