Head of Engineering & AI

• Department: Product & Engineering

• Compensation: Negotiable

• Primary Tech Stack: Node.js, React.js, Python, Modern AI/LLM Infrastructure, Distributed Cloud Systems

Platform Vision & Mission

We are building an intelligent parenting platform designed to guide families through the complexities of adolescence by unifying longitudinal behavioral insights, AI coaching, human mentorship, and real-world interventions.

Rather than treating AI as a secondary feature or an isolated chat assistant, we are architecting an AI-native ecosystem. In this environment, AI serves as the core intelligence engine that continuously interprets family dynamics, identifies knowledge gaps, selects optimal interventions, and orchestrates actions across parents, mentors, and the application itself. We are seeking an engineering leader to build and scale this core engine.

Role Overview

As Head of Engineering & AI, you will hold end-to-end accountability for our engineering organization, technical architecture, system reliability, and the evolution of our AI infrastructure.

This is an active building and transformation role. You will inherit an operating product, a working engineering team, production systems, technical debt, and an evolving AI roadmap. Your objective is to establish technical ground truth quickly, eliminate system vulnerabilities, raise the bar for engineering execution, and build the foundational architecture required for our next phase of growth.

You will report directly to the CEO and partner closely with leaders across Product, Design, QA, Operations, and Behavioral Science.

What You Will Drive & Own

• Engineering Organization & Culture: Foster a high-agency, high-throughput engineering culture defined by clear accountability, strong technical discipline, and predictable delivery schedules.

• Production Stability & Reliability: Identify system fragility, implement robust observability and incident management frameworks, resolve recurring failures, and embed reliability into daily engineering practices rather than continuous firefighting.

• AI Architecture & Infrastructure: Lead the design and implementation of our AI capabilities, including agentic workflows, behavioral intelligence engines, personalization algorithms, tool integration, model orchestration, retrieval (RAG), context memory, evaluation pipelines, and AI cloud infrastructure.

• AI-Native Product Delivery: Partner with Product leadership to turn emerging AI capabilities into seamless, intuitive user experiences rather than basic demo features or standalone chatbots.

• AI-Augmented Engineering: Fundamentally transform internal software development by embedding AI tools into coding, automated testing, debugging, code reviews, QA workflows, internal tooling, and knowledge sharing.

• Architectural Strategy: Make data-backed decisions on refactoring, rebuilding, or maintaining core systems—balancing legacy Node.js backend services and React.js frontend components with new data pipelines and AI frameworks.

• Talent Management: Assess existing team capabilities fairly, mentor high-potential engineers, recruit exceptional talent, and decisively address performance gaps when necessary.

• Quality Assurance: Collaborate with QA leadership to build comprehensive automated testing, defect prevention systems, and confident release pipelines.

• Hands-On Technical Execution: Maintain deep visibility into the codebase, infrastructure, and operational incidents to understand true system behavior beyond high-level dashboard metrics.

Our Expectations for AI Expertise

We require a leader whose AI experience extends far beyond making simple API calls to commercial LLMs. You should be actively solving complex engineering challenges, such as:

• Architecting agents that plan, reason, retain context over time, utilize external tools, and execute multi-step actions reliably.

• Evaluating non-deterministic AI systems systematically in live production environments.

• Determining the right balance between prompt engineering, RAG, fine-tuning, structured workflows, and traditional software logic.

• Optimizing model performance, latency, unit economics, system reliability, and user safety.

• Converting longitudinal behavioral data into actionable intelligence.

• Designing modular software architectures that remain resilient as foundational models evolve rapidly.

• Leveraging AI to fundamentally elevate engineering output, quality, and team velocity.

Candidate Profile & Experience Requirements

• Overall Experience: 8+ years of total experience in software engineering and technical leadership roles (Head of Engineering, VP of Engineering, Engineering Director, Technical Co-Founder, or Senior Technical Lead) in high-growth technology environments.

• EdTech Domain Expertise: Minimum 3+ years of hands-on experience building, scaling, or managing products within the EdTech (Educational Technology) or learning ecosystem domain.

• Full-Stack & System Fundamentals: Strong technical foundation across distributed systems, with hands-on mastery of Node.js for backend microservices/APIs and React.js for responsive frontend web applications.

• Production AI System Delivery: Practical experience architecting and deploying production-grade AI/LLM applications utilizing modern engineering patterns (agents, RAG, tool integration, model orchestration, evaluations, and AI observability).

• Live System Ownership: Demonstrated experience managing live production environments with active users, with a track record of stabilizing and refactoring complex or inherited codebases.

• Pragmatic Architectural Judgment: Strong technical decision-making skills across backend, frontend, and infrastructure, paired with the discipline to avoid rewriting systems purely for personal aesthetic preference.

• People & Organizational Acumen: Ability to accurately identify whether execution bottlenecks stem from human, process, or architectural issues, combined with proven success in recruiting and developing top engineering talent.

• Executive Presence & Courage: Willingness to challenge executive stakeholders on strategic and technical decisions, paired with the maturity to align, commit, and execute once a final decision is made.