About the Client

They are a premium, venture-backed healthtech that seamlessly merges AI-enabled technology with an ultra-high-end human concierge service. Their mission is to maximize their clients' healthspan by leveraging multi-disciplinary specialties across science, medicine, physiology, nutrition, and supplementation.

The Role

They are seeking a Head of Engineering to own their technology ecosystem end-to-end. As the most senior  technical leader, you will be directly responsible for architecture, delivery, reliability, security, healthcare-grade compliance, and growing the engineering team.

This is a high-ownership role designed for a "deep thinker" who is naturally curious about health data and has scaled complex systems from zero-to-one. You will establish the technical direction, cultivate a high-throughput prototyping culture, and remain deeply hands-on with the most complex architectural and system design decisions.

Ideally, you are someone who is personally passionate about health optimization, longevity, and bio-hacking - someone who already tracks their own data (wearables, biomarkers) and understands the nuances of the healthtech landscape from a user perspective.

Key Responsibilities

• Technical Ownership & Culture: Own the architecture, roadmap execution, reliability, and security of the platform. Build a robust engineering culture that leverages modern cloud infrastructure and the evolving AI landscape.

• Cross-Functional Collaboration: Partner closely with founders, product management, clinical specialists, and external provider partners to translate complex health concepts into seamless software solutions.

• Core Platform Components to Oversee:

  • Continuous Reasoning Engine: Multi-modal data ingestion (labs, biomarkers, wearable streams, imaging, self-reports) to identify real biological patterns while filtering out noise and cross-referencing global scientific literature.

  • Recommendation Engine: A module matching medical and health telemetry against scientific knowledge, biased by user preferences, to generate optimized intervention and diagnostics pipelines.

  • Unified Data Model: Transforming highly unstructured, fragmented inputs into a coherent, queryable profile with distinct confidence scoring.

  • Multi-Role UX Ecosystem: Overseeing both the consumer-facing app (optimized for behavior change and habit tracking) and the provider/partner portals used by distributed teams (coaches, nutritionists, clinicians).

  • Granular Access & Security Layer: A robust, least-privilege, role-based data governance framework tailored to highly confidential health and PHI-equivalent data.

Key Requirements

Must-Haves

• Strong Data Engineering Experience: Proven track record building robust data pipelines to collect, structure, clean, and normalize highly heterogeneous clinical data and time-series data syntheses.

• Proven Zero-to-One Experience: Experience launching prototypes, MVPs, and driving rapid iteration cycles with a distinct product discovery mindset.

• Technical Hands-On Capability: Highly capable individual contributor when needed. You must be willing and able to jump into coding, system design, and architecture directly, making pragmatic technical trade-offs.

• People Leadership: Proven success building, hiring, and leading small engineering teams within a startup or fast-paced, early-stage environment.

• Security & Compliance Awareness: Experience designing advanced permission systems and handling PHI-equivalent data under strict security guidelines.

• Stakeholder Management: Exceptional communication skills with the ability to manage relationships across a diverse group of founders, product leads, and medical professionals.

Good-to-Haves

• AI & Knowledge Engineering: Direct experience building cognition engines, LLM/inference layers, or clinical decision support components.

• Healthcare/Clinical Data Familiarity: Deep familiarity (or strong adjacent exposure) with clinical data workflows. Ability to interpret clinical notes, provider reports, and deeply understand data quality, error modes, and privacy/compliance implications.

• Practical Data Science/ML: Hands-on experience navigating the full loop of data features and insights.

• Health Integration Ecosystems: Past experience integrating wearables (biometric data streams), third-party health data feeds, or EMR/health record systems (e.g., FHIR / HL7).

• Full-Stack & Cloud Infrastructure: Broad domain experience spanning cloud infrastructure, data analytics, and user-facing product engineering.

• Regional Scaling Experience: Experience hiring and operating development teams within Southeast Asia.

• Early-Stage Culture Fit: An appetite for early-stage startup dynamics and a strong motivation toward equity upside.