Head of Engineering

About Parker

Parker is the AI marketing brain for every business in the world.

We launched in February and went viral. Parker now advises on over $1 billion in annual ad spend across hundreds of brands, including some of the largest consumer companies in the world.

See our launch here: https://x.com/heyparkerdotai/status/2016221027093905561?s=20

It's never been easier to start a business. But attention is limited. Every business owner alive is fighting for the same shrinking pool of customer attention, and most of them are losing. They guess at messaging. They copy competitors. They burn cash on ads that die in three days. They have no idea what their customers actually want to hear.

Parker sits in the middle of that fight. We help business owners win attention efficiently across paid, organic, retention, forecasting — every surface of the marketing funnel. The marketing function at every company on earth is our addressable surface area. If we win this position, Parker is a generational company.

The work spans the deepest parts of the technical stack and the deepest parts of human psychology, at the same time. On the technical side, we're building agentic AI systems that ingest millions of signals — customer reviews, ad performance data, organic content, comments, transcripts — and turn them into creative that converts. We're doing original research to imbue our models with real creative taste. Not just pattern-matching on customer language, but internalizing the principles of great creative strategy. We want Parker to have judgment, not just recall.

On the psychology side, we're studying what actually moves people. Why a phrase like "dad bod" sells more jeans than "athletic fit." Why some ads stop the scroll and others die. The work sits at the intersection of LLM research, marketing science, and consumer behavior, and almost no one is operating at this intersection seriously.

The team behind Parker is rare: top engineers, world-class creative strategists, and seasoned ad agency operators all under one roof. We're backed by OVO Fund, Unlock VP, and Hustle Fund, alongside operators from OpenAI, Anthropic, Meta, Google, and Vercel.

The vision for this role

This is a player-coach role. You'll spend meaningful time in the code — shipping features, architecting systems, reviewing PRs — while also leading the team and setting technical direction. You're hands-on, not hands-off.

Parker today is great. Parker tomorrow is true AI agent orchestration — autonomous agents that research customer trends, write ad strategy, generate creative, run tests, and double down on what's working, all in concert. No human in the loop except to set direction.

Most of the industry is still gluing together prompts and calling it an agent. We're past that. You'd own the architecture of our agentic systems — the orchestration layer, the eval harnesses, the memory systems, the failure modes. The work is hard and there isn't a playbook for it.

There's also a research layer. We're finetuning our own models on creative taste — what language converts, what stops the scroll, what doesn't. Real ML research, with a short line to product. You'd lead it.

The bigger bet: we want to keep the engineering team small and have AI agents do most of the dev work. The team's job is managing those agents, not writing every line. Engineers as AI managers. Very few companies are actually running this way. You'd be the one setting us up to.

Why this is an engineer's dream

A small AI-leveraged engineering team is only half the story. The other half is distribution. Half our team is made up of world-class ad agency founders with serious reach into the market. That means everything you build lands in the hands of real customers immediately. No sitting on shelfware. No begging for design partners. You ship it, and it goes straight to brands spending real money to grow.

Most AI engineering roles give you the tech but not the distribution. Most adtech roles give you the distribution but not the AI. Parker is both, and the combination is rare.

What you'll own

• Reporting directly to the CEO (a Stanford AI engineer by trade)
• Technical leadership of the engineering team (currently 5 people) — setting direction, running hiring, growing people
• Technical leadership of our non-human AI engineering team — the agents doing increasing amounts of the actual dev work, and the systems that let them
• Architecture of Parker's AI agent systems — orchestration, eval harnesses, prompt infrastructure, memory, and the pipelines that power them
• ETL and data pipelines processing large volumes of customer review, ad performance, and organic social data
• Infrastructure reliability and scale — we're growing fast and the product needs to keep up
• End-to-end product delivery across frontend, backend, and API orchestration — including iterating directly with customers, taking their feedback into the code, and shipping improvements fast
• Using the latest AI coding tooling (Claude Code, Cursor, etc.) to ship at 10x speed — this is how we work, not optional

Our stack

TypeScript end-to-end. Mastra SDK for agent orchestration, Supabase, Qdrant, Redis, Temporal, Langfuse, Vercel AI SDK, GCP. We use whatever the best tool for the job is — often something released in the last six months.

You don't need to have used all of these, but you should be deeply comfortable with the patterns: agent orchestration, vector search, durable workflows, eval and observability for LLM systems, scalable architecture, and the devops side — CI/CD, infra-as-code, monitoring, incident response.

What we're looking for

AI-native engineer. You've built production multi-agent systems and complex LLM orchestration — not just prototypes. You understand prompt engineering, agent architectures, RAG pipelines, and the tradeoffs between them.

Experienced architect. You've designed and scaled systems that handle real load. You know how to build ETL pipelines, design for reliability, and make infrastructure decisions that don't need to be unwound in six months.

Strong IC who leads. You write code every day and you're great at it. You also know how to set technical direction, run a team, and make people around you better.

Strong communicator. You can explain complex technical decisions clearly to anyone in the room — engineers, customers, investors, the rest of the team. You're comfortable talking with customers directly and folding what you hear back into the work.

AI tooling power user. Claude Code with sub-agents and custom MCP servers, Cursor with full repo context, Codex, Devin, Windsurf — whatever the frontier looks like the week you read this. You're running multi-agent workflows, building your own internal tooling on top of these systems, and you expect your team to do the same.

Startup-minded. You've either founded something, been an early employee at a fast-growing startup, or otherwise know what it feels like to build with urgency and limited resources. You don't need a playbook.

Plugged into dense talent networks. You came up somewhere that puts you near great people — a top school, a well-known tech company, a serious research lab. Hiring is going to matter, and the people you already know are part of how we win.

Bonus points

• You've led a small eng team through a period of rapid growth
• You have strong opinions about eval infrastructure and test harnesses for AI systems
• You've worked in performance marketing, adtech, or a data-heavy consumer product

What this is not

• A pure people-manager job. You'll be in the code.
• A corporate engineering leadership position. We move fast and expect you to.

To apply

Send us two things:

1. Introduce yourself. Who you are, why this role interests you, and why you'd be great at it. Specific beats long.

2. A short written answer (a few sentences) to one of these:

   • What's the most interesting agentic system you've seen built recently, and what makes it good?
   • What do you think Parker should build next?

If you have a personal site, Twitter, or GitHub, include a link. Resume optional — attach if you want.