Product Engineer - core role for startups in the age of AI

Product engineer goes all the way to deliver results to their customer

TL;DR: AI has changed what a engineer can get done; today the highest impact early hire in a startup is a Product Engineer.

The "Product Engineer (PE)", as first seen at scale in OpenAI, is effectively a Founder in a Box for a single product. They are expected to be empathetic to the user, and obsessed with the "feedback loop" of shipping and learning.

Their scope is:

Domain Knowledge: PE needs to understand the business domain and customers, and product priorities. In many cases they need to become SME (Subject Matter Expert) or find resources and people in their network to bridge any gaps; otherwise they will not know how and what to build.

AI helps them here by quickly doing research for them; accelerating their learning in the business domain. But their knowledge of domain and critical thinking must be enough to tell when AI is wrong and double check.

Technical Implementation: PE needs broad understanding of technology to guide AI in implementing it / implementing some parts themselves / picking right solutions to have easiest code that can get the job done.

AI helps them by telling what existing solutions exist as cloud services / libraries / design patterns, and coding it quickly and in languages that they might not fully master. But their technical skills are critical so that they can ensure their code is tested for the right things, no architectural blunders occur and that code complexity stays in check.

Communication: Since PE knows what to do and how, they are the only ones who can estimate realistic timelines for the milestones of their project. They communicate to the customers to set their expectations; They also communicate to the rest of the org to get unblocked when needed.

AI helps them communicate more efficiently. Internally: writing internal emails and documentation, summarizing any info they need. Externally: they can quickly prototype demos, write blog posts and/or find channels where they can find customers.

The Full-stack got even fuller

The Product Engineer combines Design + Coding + QA + Product Manager + Marketing into one person. How is this sustainable without burnout? The secret is Scope. Instead of being narrow in competence as old roles were, they can be narrow in domain. Just focusing on one project at a time, or couple of very tightly related projects gives them clarify and removes the context switching. They will no longer switch projects, they will switch tasks in the one project that is their one and only goal at a time.

AI allows to Skip the Communication Tax

The reason why this is superior is that now that AI can help with many tasks and especially application level coding, it no longer makes sense to carry the communication overhead burden ("Communication Tax") of separate people for the functioning unit of product delivery.

The bar for specialized knowledge needed to ship software was lowering for a long time now. How? Cloud tools, better programming languages and frameworks, documentation and search are some examples. Think how in the times C++ was the dominant language, common perception was that you had to be a nerd and maybe kind of a genius to be good at software. Then came python and js, they've lowered the bar. Then cloud providers have offered solutions for most core problems of scalable software. And now AI came around to guide people to which cloud product and programming language / library to use and how for which usecase. AI advice can be imperfect, but it hugely helps to fill the gaps, that before meant that a single engineer was blocked without a help from teammate with different skills.

So the bar is now at the point where you need core concepts, like where code runs and where data is stored, how web works - DNS, frontend / backend, endpoints, how to call a LLM. But you don't have to be able to implement them from scratch or anything like that - you just need to know how they should come together to solve a business problem.

Who shines in this role

So what qualities and skills does it require to be a great PE?

This is not a junior role. It requires a specific profile:

Coding Affinity is Non-Negotiable: You cannot master AI-assisted coding without foundational experience. You need the intuition to know when the AI is hallucinating or leading you down a rabbit hole.
Ownership Mindset: Quick learning and commitment to reach this high performance bar by embracing the new tools.
Shipping Experience: We want builders who have a history of shipping software, not just writing code. But sometimes

Anti-patterns:

Rigid Process: You shouldn't hire someone who wants to follow a rigid process because they will resist the speed involved in the PE role.
Small Time Budget: Being part-time in this role might be hard for PE because they still need to keep a lot in their head; in a classical role you could make someone narrow in both competence and domain. By definition that would not be a PE.

How it scales

But what if your project is very important and you want to have more people working on it?

Phase 1: The Jazz Band (2-4 People) A small group of Product Engineers works with fluid responsibilities. Trust is high, hierarchy is flat, and everyone naturally fills niches based on preference.

Phase 2: Cellular Independence (5-20 People) As you scale, you split the product into distinct domains.

Engineer A owns Onboarding.
Engineer B owns Billing & Subscriptions.
Engineer C owns Core AI Workflow. Each unit sets their own timelines and communicates directly with stakeholders.

The Enabler: The Platform Team

To prevent "Infrastructure Drift"—where every engineer picks a different stack—you eventually introduce a Platform Team. Great start would be making the stack built for your most successful project a template for others to follow. This should provide your team with:

Paved Roads: They build the standard, easy way to deploy code and manage databases. People will have easier time helping each other because projects use similar tech and patterns.
Guardrails, not Gates: They provide tools that automatically ensure security and quality.
Design Systems: They maintain the UI library so Product Engineers can build beautiful interfaces without being expert designers.

What will come next

As AI coding tools will improve in scope and quality, it seems likely that in a year or two (2028?) coding experience might no longer be a hard requirement for this role. What will be even more important is clear thinking about the results you can achieve and what steps can take you there. This is a builders paradise - you get more and more help to implement any idea you get. So what will be the next scarce resource, the next bottle neck? I don't think it will be coming up with good ideas, because you can try ideas faster. Its the will to iterate, learn and pivot which will determine who gets ahead.

References

Example of actual PE job description from oai (cached so it isn't lost when job gets removed): https://blog.covenance.ai/example-product-engineer-job/