Fractional CTO / Founding CTO

About the role

Borderless builds the decision-resolution engine for regulated AI in healthcare: an orchestration layer that captures model outputs → human interventions → final attestation → real-world outcomes and turns that loop into repeatable training data, evaluation, and safe automation. We are not optimizing for “feature breadth” or an EHR UI. We are building the trust + training substrate that makes high-stakes automation measurable, governable, and scalable.

This is a “platform CTO” role centered on orchestration, provenance, and evaluation—the system that turns messy real-world decisions into a training/evidence flywheel.

What you will own (non-negotiable)

• CDO v1 spec + implementation (append-only, replayable, auditable)
• Orchestration framework (routing, queues, retries, idempotency, approvals, policy gates)
• HITL instrumentation (human deltas as first-class events)
• Outcome-binding pipeline (link decisions to objective results)
• Eval + trust substrate (metrics, dashboards, red-team/rollback, provenance)
• Security + governance-by-design (RBAC, audit logs, encryption, access review)

Initial wedge (expected focus)

Medical coding → claim submission → payment outcome (starting in outpatient dermatology) because it provides:

• objective/fast feedback
• high economic leverage
• natural human review loop
• measurable ground truth

Responsibilities

Architecture & sequencing

• Define a staged plan that prioritizes decision→outcome resolution data over product surface area.
• Make build/buy calls for eventing, storage, workflow engine, observability, and model serving.
• Establish the “truth model”: what is append-only, what is derived, what is reversible.

Core platform build (hands-on early)

• Implement CDO schema + storage strategy (append-only log + queryable views).
• Build the orchestration runtime: task routing, HITL queues, retries, idempotency, policy checks.
• Build model invocation layer: multi-model support, versioning, replay, prompt/config provenance.
• Build policy layer: declarative constraints, thresholds, regulatory exclusions, versioned and testable.

HITL training + evaluation infrastructure

• Capture human interventions as structured events: edits, rationale, approvals, rejections.
• Build evaluation harness: golden datasets, regression suites, error taxonomy, drift monitoring and alerting, “what changed?” diff tooling across model versions/policies.
• Produce “model readiness” gates for when automation can safely increase.

Outcome binding (closed-loop)

• Design deterministic mapping from decisions to outcomes (e.g., payer adjudication results).
• Ensure outcome data is linked back to the originating decision record (lineage).

Security, compliance, and trust

• Implement: RBAC, audit trails, encryption, secrets management, environment isolation.
• Define pilot-ready posture (BAAs, incident response basics, access review cadence).

Team and execution model

• Fractional CTO: set standards, direct contractors/vendors, keep architecture coherent, deliver thin vertical slice.
• Founding CTO: recruit initial team (platform/backend, integrations, infra/security) and lead execution.

30 / 60 / 90-day deliverables

• 30 days — “Define the substrate”
  • CDO v1 written spec (fields, invariants, lineage, replay rules).
  • System architecture doc: eventing + storage + orchestration + eval.
  • Repo + CI/CD + environments + baseline observability.
• 60 days — “Close the loop”
  • Live orchestration path for one workflow (coding-focused): input context → model invocation → HITL review → final attestation
  • Outcome-binding prototype for at least one objective outcome signal (even if partial).
• 90 days — “Make it repeatable”
  • Eval harness live with golden sets + regression and dashboards.
  • Policy layer versioned and testable; safe rollout/rollback mechanics.
  • Second model or second workflow variant added with minimal incremental architecture work (proof of platform leverage).

Requirements

Must-have

• Built event-driven / workflow / orchestration systems with reliability concerns (retries, idempotency, replay).
• Deep instincts for data provenance, auditability, and governance (append-only logs, lineage).
• Experience building evaluation infrastructure (quality metrics, regressions, monitoring/drift).
• Ability to scope ruthlessly and ship thin vertical slices.

Strongly preferred

• Experience in regulated domains (healthcare/fintech) with audit trails and access controls.
• Familiarity with claims/coding/RCM workflows OR willingness to learn quickly with domain experts.
• Comfort with multi-model architectures and reproducibility (versioning, deterministic replay where possible).

Working model & comp (stage-dependent)

• Fractional (8–25 hrs/week): cash retainer + meaningful equity tied to deliverables and time commitment
• Founding CTO (full-time): founder-level equity + stage-appropriate cash

Success metrics (how we’ll judge it)

• Every decision is replayable, auditable, and outcome-bound.
• Human review is captured as structured deltas, not lost in UI.
• We can prove measurable improvement across model versions with regression discipline.
• Automation can increase safely because policy gates + HITL + rollback are real, not aspirational.