Fractional CTO / Founding CTO

Borderless — Fractional CTO / Founding CTO (HITL Orchestration + Decision Infrastructure)

Mission

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.

Role summary

You will design and ship the core infrastructure that makes Borderless valuable:

Canonical Decision Object (CDO): immutable, auditable, replayable, model-agnostic decision records.

HITL capture: event-based logging of human edits/approvals/rejections independent of UI.

Outcome binding: deterministic linkage from each decision to downstream outcomes (e.g., claim paid/denied).

Model invocation + policy layer: versioned, reproducible model calls with declarative constraints, thresholds, and exclusions.

Evaluation harness: regression tests, golden sets, dashboards, drift/quality monitoring.

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

1. 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.

2. 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.

3. 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.

4. 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).

5. Security, compliance, and trust

Implement: RBAC, audit trails, encryption, secrets management, environment isolation.

Define pilot-ready posture (BAAs, incident response basics, access review cadence).

6. 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).

Ideal candidate

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.