Head of Engineering

Technical Direction

You will own the technical direction of the platform, scale a senior engineering team, partner closely with Product and GTM, and represent Magical in high-stakes conversations with enterprise healthcare executives and technical buyers.

Systems

• Ensure the systems that make probabilistic AI reliable enough for enterprise healthcare workflows.
• Evolve the platform so customer deployments become faster, more repeatable, and more scalable.

Customer-Centric

• Customer-facing technical credibility across security, reliability, architecture, and product direction.
• Hire and develop exceptional AI-native engineers, product engineers, infrastructure engineers, and technical leaders.

What We Are Looking For

• Deep technical judgment in LLMs, agents, evals, orchestration, reliability, model behavior, and production AI systems.
• A platform builder who thinks in primitives, workflows, observability, governance, developer experience, and repeatability.
• Strong product and business judgment around deployment speed, customer trust, scalability, and defensibility.
• Experience scaling a high-caliber engineering team through an ambiguous, high-growth stage.
• Strong architecture instincts and the ability to earn respect from senior engineers without making every decision yourself.
• Fluency in enterprise security and healthcare trust requirements: HIPAA, SOC 2, PHI, permissions, auditability, logs, and data access.

Why This Matters

Agentic automation will not win in healthcare because the demos are impressive. It will win when the systems are reliable, secure, observable, governed, and economically transformative. That is the engineering problem. If we get it right, Magical becomes one of the core platforms healthcare organizations use to deploy AI workers across their most important operational workflows, even when the systems underneath are fragmented, old, and closed.

What This Role Is Not

• A traditional enterprise VP Engineering whose main strength is managing managers.
• A big-company operator who wants to add heavy process before the team needs it.
• A pure AI researcher who does not want to own production systems, customers, reliability, and business outcomes.
• A leader whose experience is limited to legacy automation models and is not excited to build AI-native systems.
• A backend-only architect who does not want to spend time with customers, GTM, and enterprise buyers.
• A sales-engineering profile who can explain the product but cannot lead architecture, execution, and engineering quality.
• A healthcare operator who understands the market but lacks world-class technical depth.