Founding CTO — AI Infrastructure & Computer Vision

We are building an industrial-grade AI system that turns design intent into manufacturable outputs for fashion and apparel production. This is not a research lab role. This is a long-horizon systems builder role.

You will own

  • End-to-end ML/CV system architecture: training → evaluation → inference → deployment

  • MLOps: data/versioning, reproducibility, CI/CD, monitoring, rollback

  • Model + geometry pipeline optimization (accuracy, stability, latency, cost)

  • Compute economics and scaling strategy (GPU utilization, orchestration, batching)

  • Cloud-agnostic infrastructure design (GCP/AWS/Azure) and enterprise deployment readiness

  • Security, data governance, and cross-region constraints in a geopolitically complex environment

  • Hiring and technical leadership: build a small elite team, set engineering culture and standards

You are

  • Strong in ML + CV fundamentals; comfortable with PyTorch and real training loops

  • Experienced shipping production ML systems (not paper-only)

  • Fluent in distributed systems and practical engineering tradeoffs

  • Calm under rapid ecosystem change; not driven by hype cycles

  • Motivated by manufacturing automation / fashion-tech / real-world constraints

Bonus

  • Geometry / graphics / CAD / vector pipelines experience

  • Experience with enterprise integrations, compliance, data residency

  • Experience scaling from 0→1 to pilots and early enterprise

Not a fit if

  • You optimize for benchmarks, visibility, or quick valuation spikes

  • You only do research and avoid production ownership

  • You pivot strategy every time a new foundation model launches

What’s already true

  • Working MVP and validated problem framing

  • Proprietary production pattern archive (data moat)

  • Manufacturer network and pilot pathways

  • Clear 12–24 month roadmap and execution cadence

Partnership
This is an equity-aligned, founding-level role. We are looking for a true technical partner, not a hired engineer.

How to apply

Send:

  1. 3 links:

    (a) shipped system,

    (b) code or technical writing,

    (c) something you’re proud of

  2. A 1-page note: “How would you architect a production ML system for manufacturable pattern outputs?”

  3. Optional: GitHub / papers / portfolio