Compensation: 3.0% – 7.0%

Founding AI Engineer | Technical Lead

Company: WONDER™
Location: Remote (U.S.) / Dallas, TX preferred
Position: Founding Team | Pre-Seed
***Compensation: Equity-first; salaried compensation anticipated following financing, subject to formal agreement *** Reports to: Founder & CEO

***Must be able to start immediately ***

About WONDER

WONDER is an early-stage AI-native relationship-intelligence company building a new infrastructure for human understanding and connection.

At the intersection of artificial intelligence, behavioral science, and interpersonal psychology, we're developing systems that help people understand themselves and one another with greater depth, accuracy, and nuance.

Our initial product reimagines dating and relationship formation through multidimensional personal understanding, adaptive AI-driven experiences, and more intentional introductions.

Our longer-term ambition extends beyond dating toward a broader relationship-intelligence ecosystem.

We have an initial product and early market interest, and are preparing for our first market launch and pre-seed financing.

We're looking for an exceptional founding technical builder to take ownership of our AI systems, product engineering, and technical execution.

The Opportunity

This is a hands-on founding engineering role for someone who wants to build sophisticated AI-native products from the ground up.

We are not looking for a traditional CTO, engineering manager, technical consultant, or someone whose primary expertise is coordinating development teams.

We are looking for someone who writes the code, architects the systems, solves the difficult technical problems, and ships the product.

You will take ownership of WONDER's technical architecture, AI infrastructure, engineering execution, and production readiness.

You will work directly with the Founder & CEO to translate ambitious product concepts into reliable, scalable, and commercially viable systems.

This role offers substantial technical autonomy and the opportunity to shape the foundational architecture of a company at its earliest stage.

You should be comfortable operating as the primary engineer, making consequential architectural decisions, and executing without an established engineering organization.

Core Responsibilities

1. AI-Native Systems Architecture & Engineering

Design, build, and maintain the foundational intelligence systems powering WONDER.

• Architect and implement production-grade applications built around large language models and modern AI infrastructure.
• Develop sophisticated LLM workflows involving structured reasoning, tool use, context management, and multi-stage inference.
• Build persistent memory and context-retrieval systems that support continuity, personalization, and evolving user understanding.
• Implement retrieval-augmented generation (RAG), semantic search, embeddings, vector databases, and hybrid retrieval where technically appropriate.
• Develop structured systems for synthesizing user-provided information into evolving, interpretable representations.
• Design and implement personalization architectures that adapt to new information, user feedback, and changing preferences.
• Build reliable orchestration layers for model interactions, background processing, data retrieval, and application logic.
• Evaluate and integrate appropriate models, APIs, frameworks, and infrastructure providers.
• Optimize model performance, latency, reliability, and inference costs.
• Establish evaluation frameworks for output quality, factual grounding, consistency, safety, and system performance.

We are particularly interested in engineers who understand the distinction between integrating an LLM API and engineering a genuinely AI-native product.

2. Behavioral Intelligence & Personalization

Develop the technical systems that enable WONDER to model, interpret, and respond to complex human information.

• Build architectures for representing multidimensional user characteristics, preferences, interpersonal patterns, and relationship goals.
• Design structured data models that support nuanced personalization without reducing users to simplistic categories.
• Develop systems capable of incorporating longitudinal user interactions, reflections, and explicit feedback.
• Implement mechanisms for updating, correcting, and reconciling user representations as new information becomes available.
• Translate behavioral-science-informed product requirements into testable computational systems.
• Develop explainable recommendation and compatibility frameworks that can be evaluated and improved over time.
• Implement safeguards against unsupported psychological inferences, misleading certainty, and inappropriate use of sensitive personal information.
• Support user visibility, consent, correction, and control over stored personal information.

The objective is not to create a system that merely generates convincing psychological language. It is to build an intelligence architecture whose outputs are useful, grounded, testable, and capable of improving through feedback.

3. Full-Stack Product Development

Own end-to-end engineering execution, from product architecture through deployment.

• Evaluate the existing MVP and determine which components should be retained, refactored, extended, or replaced.
• Build and maintain the frontend, backend, databases, APIs, authentication systems, and integrations necessary to support the product.
• Implement responsive, performant, and polished consumer-facing interfaces.
• Develop secure user account systems, onboarding experiences, conversational interfaces, and personalized product features.
• Design scalable database schemas and application services.
• Implement analytics, event tracking, monitoring, and error reporting.
• Establish automated testing, continuous integration, and deployment workflows.
• Maintain code quality, documentation, version control, and engineering standards.
• Independently diagnose and resolve technical issues across the application stack.

You must be capable of shipping complete, functioning products—not just prototypes, model demonstrations, or isolated technical components.

4. Production Infrastructure, Security & Reliability

Build the technical foundation necessary for a real consumer product handling highly personal information.

• Design secure infrastructure for storing and processing sensitive user data.
• Implement appropriate encryption, authentication, authorization, and access controls.
• Establish privacy-by-design practices, including data minimization, retention policies, deletion mechanisms, and user consent.
• Develop reliable logging, monitoring, observability, and incident-response processes.
• Evaluate third-party model providers and infrastructure dependencies for security, privacy, performance, and cost.
• Implement safeguards against prompt injection, unauthorized data exposure, and other AI-specific vulnerabilities.
• Design systems that can support early growth without unnecessary infrastructure complexity.
• Make pragmatic technical decisions consistent with an early-stage company's financial constraints.

5. Product Launch & Technical Ownership

Lead the technical execution required to bring WONDER to market.

• Establish and maintain a technical roadmap aligned with launch priorities.
• Identify the minimum architecture and feature set necessary for a reliable public beta.
• Convert product specifications into engineering milestones and working releases.
• Own technical delivery schedules, dependencies, and launch-readiness assessments.
• Conduct end-to-end testing and resolve critical issues before release.
• Deploy and maintain production systems.
• Monitor product performance and rapidly address issues following launch.
• Use product analytics and user feedback to prioritize technical improvements.
• Establish a scalable foundation for subsequent product iterations and market expansion.

You will be accountable for the technical delivery and operation of the product, not simply for advising on how it should be built.

Initial 90-Day Expectations

The successful candidate will establish a mutually agreed technical execution plan with measurable deliverables.

First 30 Days — Architecture & Assessment

• Conduct a technical assessment of the existing product and infrastructure.
• Identify architectural weaknesses, technical debt, security risks, and launch blockers.
• Produce a prioritized technical roadmap and implementation plan.
• Establish the development environment, deployment processes, and essential engineering standards.
• Validate the proposed AI architecture through working technical implementations.

Days 31–60 — Core Systems Development

• Build, integrate, or materially improve the priority AI systems.
• Implement essential personalization, memory, and user-data workflows.
• Complete the highest-priority application functionality required for beta.
• Establish automated testing, monitoring, and initial AI evaluation procedures.
• Demonstrate functioning end-to-end user experiences.

Days 61–90 — Production Readiness & Launch

• Complete the agreed beta-critical engineering milestones.
• Resolve critical reliability, privacy, and security issues.
• Validate performance, inference costs, and system behavior under realistic usage conditions.
• Deploy the agreed release to a production or controlled-beta environment.
• Establish monitoring, incident handling, and a post-launch engineering roadmap.

Actual launch timing will depend on product scope, available resources, and technical readiness. Success will be assessed against agreed deliverables, not arbitrary feature counts.

Required Qualifications

We care substantially more about what you've built than where you've worked or what degree you hold.

Technical Experience

• Demonstrated experience independently building and deploying production software products.
• Strong hands-on experience developing AI-native applications using modern LLMs and associated infrastructure.
• Advanced proficiency in Python and/or TypeScript, with the ability to work effectively across the full application stack.
• Practical experience with LLM APIs, orchestration frameworks, structured outputs, tool calling, and multi-step AI workflows.
• Experience with embeddings, vector search, retrieval systems, and persistent context or memory architectures.
• Strong understanding of backend engineering, API design, relational databases, and cloud infrastructure.
• Experience with production deployment, monitoring, debugging, testing, and performance optimization.
• Ability to evaluate architectural trade-offs involving latency, inference cost, model capability, privacy, and reliability.
• Strong software engineering fundamentals and the ability to write maintainable, production-quality code.

Execution & Ownership

• Evidence of personally shipping products used by real people.
• Ability to take an ambiguous product requirement and independently translate it into a working technical implementation.
• Experience owning systems beyond initial development, including deployment, maintenance, and iteration.
• Ability to prioritize ruthlessly, work within resource constraints, and distinguish essential engineering from premature complexity.
• Comfort making technical decisions without relying on a large engineering team.
• Strong communication skills and the ability to explain technical decisions to a non-engineering founder.

Particularly Valuable Experience

• Building AI products involving persistent user memory, longitudinal personalization, or adaptive conversational systems.
• Developing recommendation, matching, ranking, or compatibility systems.
• Working with structured and unstructured behavioral or conversational data.
• Experience with model evaluation, fine-tuning, inference optimization, or custom model pipelines.
• Building consumer AI applications with meaningful engagement and retention.
• Experience with privacy-sensitive products, consent systems, and secure personal-data infrastructure.
• Prior founding engineer experience or independently launching a startup.
• Experience building and shipping mobile applications.
• Familiarity with behavioral science, psychology, human-computer interaction, or computational social science.

Direct experience training foundation models is not required. However, candidates must understand AI systems deeply enough to make informed architectural decisions beyond basic prompt engineering and API integration.

Who We're Looking For

You're a builder before anything else.

You've probably built products independently, experimented extensively with emerging AI capabilities, and spent considerable time solving engineering problems without being asked.

You have strong opinions about architecture, but you're pragmatic enough to change your mind when the evidence warrants it.

You can move between complex AI systems engineering and the practical realities of getting a consumer product into users' hands.

You're comfortable being the person responsible when something breaks—and capable of fixing it.

You don't need a large team, elaborate processes, or perfect specifications to make meaningful progress.

You enjoy the intellectual challenge of turning an unconventional idea into functioning technology.

Most importantly, you're interested in building AI that meaningfully improves human experiences rather than simply automating existing workflows.

Compensation & Founding Opportunity

WONDER is currently a pre-seed company.

This is an equity-first opportunity intended for a technically exceptional individual interested in joining at the founding stage.

• Meaningful equity participation, negotiated based on experience, commitment, and scope of responsibility.
• Equity subject to vesting and definitive agreements.
• No guaranteed cash compensation is currently available.
• A salaried arrangement is anticipated following sufficient financing, subject to budget, applicable employment law, and mutually agreed terms.
• Initial commitment and the potential transition to a full-time role will be discussed before engagement.
• Upon funding, a competitive market comparable will be offered: $180K-$210K.

This role involves the financial uncertainty and execution demands associated with building an early-stage venture.

We are seeking someone interested in meaningful technical ownership and long-term company value creation.

How to Apply

Please provide:

1. GitHub, portfolio, or shipped products. Show us something you personally built. Live products and functioning technical systems are strongly preferred.
2. Your most technically sophisticated AI project. Explain its architecture, your individual contribution, the hardest engineering problem, and how you solved it.
3. Production experience. Describe an AI-enabled product you've launched. Include usage, scale, reliability, or other measurable outcomes where available.
4. Your approach to AI memory and personalization. Briefly explain how you would architect a system that develops an evolving understanding of users while preserving privacy, accuracy, and user control.
5. Availability and motivation. Explain your realistic commitment before financing and why an equity-first founding opportunity interests you.

Candidates may be asked to participate in a technical architecture discussion and walkthrough of previously shipped work.

WONDER™
There's no greater intimacy than being understood.