Staff/Principal Product Engineer (AI-Native Builder)

Traditional engineering roles hand you a task and ask you to implement it. This one hands you a problem and a fleet of AI agents and asks you to ship the solution. 

The Product Engineer / Builder is a Staff/Principal level individual contributor role, for someone who takes a defined problem and owns the solution end to end. You understand the customer and the problem, then design, build, and ship working software, using AI agents as your primary means of turning direction into product. You combine deep engineering craft with genuine product instinct, and act as a force multiplier rather than a single pair of hands.  

How You’ll Work 

Given a problem to solve, you shape the approach and use AI to bring it to life by directing several AI agents in parallel and rigorously reviewing what they produce. An idea sketched in the morning can be a functional prototype by lunch and a working product by the end of the week. You think like a product manager, care like a designer, and build like an engineer. 

This is not a role for someone who waits to be told exactly what to type. 

You bring taste and judgment to every decision. It’s your responsibility to know what to build, what not to build, and to prove — through evals, testing, and review — that AI-generated work is reliable enough to ship. You measure success in customer and business outcomes, not tasks completed. 

Build on What Works, Invent Where It Counts 

Build rapidly by composing solutions from the best available tools, whether internal or external, and apply your own innovation where it matters most. That means drawing on proven platforms, components, services, and models instead of building everything from scratch, then adding original thinking where there are gaps to close, experiences to elevate, or clear opportunities to differentiate. You know when to leverage what already works, when to extend it, and when to reinvent a piece entirely — always with the goal of creating meaningful product advantage, not just more code. 

Setting the Bar for AI-Native Engineering 

As Diligent accelerates its transformation into an AI-native organization, you set the standard for what AI-native engineering looks like in a regulated, security- and compliance-conscious environment. You establish the patterns, prompts, evals, and guardrails that colleagues reuse, and you raise the leverage of every team you touch. You uphold Diligent’s bar for quality, security, and compliance, making sure AI-native work meets the standards our customers rely on. 

These are the essentials you’ll need to get an interview: 

Apply if you… 

• Have a track record of driving complex products end to end — building and shipping reliable, production-grade software with visible impact  

• Have strong full-stack engineering fundamentals and the judgment and experience to make sound architectural calls 

• Have an integrator’s mindset: you see how the pieces fit together, favor proven building blocks over reinventing them, and design so your work compounds across teams rather than being rebuilt 

• Are genuinely fluent with modern AI tools and agents, including running several agents in parallel, context-switching between them without losing thread state, knowing when to intervene, composing them into pipelines and picking the right model or tool for a given task 

• Can evaluate AI output with engineering rigor, spotting where it breaks and knowing when it’s reliable enough to ship 

• Have strong product intuition and are comfortable talking directly to customers and users, taking projects from brief to completion 

• Have a bias to ship: you thrive in ambiguity, pick up dropped balls, and move outcomes rather than tasks 

• Care deeply about quality, security, and reliability 

• Communicate clearly, and can explain not just what you built, but why it matters 

• Lift the people around you by mentoring engineers, sharing what works, and multiplying the output of every team you touch 

• Have defined AI-native engineering practices, evals, or internal tooling that other teams went on to adopt 

• Have designed or orchestrated multi-agent workflows successfully 

• Can point to things you’ve built and shipped - side projects very much count 

#LI-Hybrid