About TeenCare

TeenCare is building an AI-powered parenting intelligence platform for families navigating adolescence.

Our product combines longitudinal behavioral understanding, AI coaching, human mentorship, and real-world interventions to help parents understand their child more deeply and know what to do next.

We are now moving from AI-enabled to AI-native.

AI is not a feature sitting inside TeenCare. We are building toward a system where AI continuously understands each family, identifies what it still needs to learn, decides the next best intervention, and coordinates actions across parents, mentors, and the product itself.

We are looking for the engineering leader who can build that future.

The Role

We are hiring a Head of Engineering & AI to own TeenCare’s engineering organization, technical architecture, production reliability, and AI-native transformation.

This is not a role for someone who wants to inherit a mature engineering machine.

You will help build the machine.

You will inherit an existing product, engineering team, production systems, technical debt, an evolving AI stack, and an ambitious roadmap. Your job is to establish technical truth quickly, improve reliability and delivery, raise the quality of the team, and build the architecture required for TeenCare’s next stage.

You will report directly to the CEO and work closely with Product, Design, QA, Operations, and our behavioral science / research contributors.

What You Will Own

Engineering leadership. Build a high-performance engineering organization with clear ownership, strong technical standards, high agency, and predictable delivery.

Production reliability. Understand where the system is fragile, establish strong observability and incident management, reduce recurring failures, and make reliability an engineering discipline rather than a firefighting exercise.

AI architecture. Lead the technical architecture behind TeenCare’s AI systems, including agentic workflows, behavioral intelligence, personalization, tool use, model orchestration, retrieval, memory, evaluation, and production AI infrastructure.

AI-native product development. Work with Product to turn emerging AI capabilities into real product experiences rather than demos or isolated chat features.

AI-native engineering. Build an engineering organization where AI materially changes how software itself is built: development, testing, debugging, code review, QA, observability, internal tooling, and knowledge management.

Architecture and technical strategy. Make evidence-based decisions on what to rebuild, what to refactor, what to preserve, and where architectural investment will materially improve the product.

People. Assess the existing team fairly, develop strong engineers, recruit exceptional talent where needed, and make difficult performance decisions when necessary.

Quality. Work in close partnership with QA leadership to create strong release confidence, automated testing, defect prevention, and production feedback loops.

Technical execution. Stay close enough to the code, architecture, infrastructure, and incidents to know what is actually happening—not only what dashboards and reports say.

What “AI-Native” Means to Us

We are not looking for someone whose AI experience consists of calling an LLM API.

You should already be thinking deeply about questions such as:

• How should agents plan, reason, use tools, retain memory, and take actions?
• How do we evaluate non-deterministic AI systems reliably?
• When should we use prompting, RAG, fine-tuning, structured workflows, or traditional software?
• How do we balance model quality, latency, cost, reliability, and safety?
• How should behavioral and longitudinal user data become useful intelligence?
• How do we build systems that improve as models change rapidly?
• How should AI change the engineering organization itself?

You do not need to be an academic ML researcher.

You do need to be someone who actively uses frontier AI systems, understands modern AI engineering deeply enough to make architectural decisions, and has shipped AI into production.

What We’re Looking For

You are likely a strong Head of Engineering, Engineering Director, VP Engineering, technical co-founder, or senior engineering leader from a high-growth technology company.

We care much more about what you have actually built and led than your previous title.

You should have:

• Strong software engineering fundamentals and technical depth.
• Experience leading an engineering team of meaningful size.
• Experience owning production systems with real users.
• A record of shipping products rapidly without sacrificing engineering quality.
• Experience diagnosing and improving messy or inherited systems.
• Strong architecture judgment across backend, infrastructure, data, and modern application systems.
• Hands-on experience building production AI / LLM systems.
• Strong familiarity with modern AI engineering patterns such as agents, RAG, tool use, evaluations, structured outputs, model orchestration, and AI observability.
• Experience hiring and developing strong engineers.
• The ability to distinguish a people problem from a process problem from an architecture problem.
• The judgment to avoid rewriting systems simply because you would have built them differently.
• The backbone to challenge the CEO and other leaders when you disagree—and the maturity to test assumptions, align, decide, and execute afterward.

The Kind of Leader Who Will Win Here

You have high standards without becoming ideological about process.

You care about outcomes more than ceremony.

You can enter an imperfect organization without needing the organization to become perfect before you can perform.

When something does not work, your instinct is:

understand it → challenge it → redesign it → prove the better system.

Not simply accept it.

Not simply complain about it.

You are comfortable moving between:

architecture review → production incident → hiring interview → product discussion → engineering 1:1 → AI prototype.

First 90 Days

In your first 2 weeks, we expect you to establish a clear view of:

• the real state of our architecture and production systems;
• the root causes behind our major reliability issues;
• engineering ownership and capability;
• the quality and risks of our current codebase;
• the right technical path for our next-generation platform;
• the architecture required for TeenCare’s AI-native future.

By the end of your first 90 days, we expect materially stronger:

reliability, engineering ownership, release quality, delivery predictability, team capability, and technical direction.

Why TeenCare

You will have the opportunity to build something technically unusual.

We are building a longitudinal intelligence system for families: a system that continuously learns, reasons about what it does and does not know, chooses what information to collect next, and translates that understanding into personalized interventions through both AI and humans.

The engineering problems span: