Principal Engineer

We are seeking a Principal Engineer who sets technical direction by example. An architect who still writes code, reviews code, and makes the engineers around them better. This is not a role for someone who has moved away from the keyboard. It is a role for an engineer who has shipped production systems at scale, knows the million small details that separate a demo from a product customers depend on.

You will set architectural direction across our products and platforms. You will partner with our existing engineers, challenge their thinking, and ensure the systems we build are the best-architected they can be: simple, modular, resilient, and built to evolve. You will spend real time in code reviews, in design documents, and at the keyboard yourself. And you will invest heavily in the engineers around you: the strength of the team you grow is the truest measure of your success in this role.

You understand that AI has fundamentally changed the job of a Principal Engineer for the better. The best practitioners today ship more, review more, prototype more, and mentor more because they treat AI as an amplifier, not a threat. You will bring that mindset to DeepRunner, and you will help the rest of the engineering organisation get better at it.

Responsibilities:

• Set architectural direction across our products and platforms.
• Drive the decisions that matter: service boundaries, data models, API contracts, and the shape of the systems we will still be living with in three years.
• Write code. Ship code. Review code. Your tenure here will be measured in commits and architecture decisions, not slide decks.
• Raise the bar on code reviews. Every review is a teaching moment.
• Define the standards, enforce them, and explain the why so they stick.
• Mentor and grow the engineers around you.
• Develop your peers and your juniors into stronger engineers. This is a core responsibility of the role, not a side activity.
• Ship production software with full production responsibility: edge cases, failure modes, backwards compatibility, migrations, observability hooks.
• Always insist on the highest standards spanning architecture, development, deployment, and operational excellence.
• Use AI in your daily work the way a modern Principal Engineer should: to prototype faster, review more code, explore more alternatives, and multiply your own reach.

Requirements:

• 10+ years of hands-on software engineering with hands-on backend experience and a clear track record of shipping production enterprise systems that customers depend on.
• Experience designing large-scale distributed systems.
• Exemplary practitioner: still writes production-quality code, still does code reviews, and takes pride in it.
• Strong architectural judgment. Can design systems that are simple, modular, and resilient. Can spot over-engineering and under-engineering with equal confidence. Can explain trade-offs in plain language.
• Proven history of mentoring and developing other engineers, not just shipping personal work. Concrete examples of engineers whose growth you meaningfully accelerated.
• Deep grasp of the million details of shipping production software: edge cases, failure modes, backwards compatibility, migrations, zero-downtime changes.
• Active engagement with open source. Meaningful contributions to OSS projects, not just consumption. OSS is how great engineers learn from and teach each other, and we want that culture in-house.
• Fluent with modern AI coding assistants (Claude Code, Cursor, or similar). You understand that AI has changed the job of a Principal Engineer for the better, and you can articulate exactly how.
• Excellent written and spoken communication. Clarity is a core engineering skill at DeepRunner, not a soft skill.

Preferred:

• Startup experience. You have worked where you had to build, ship, and operate with a small team and a short runway.
• Track record of introducing architectural patterns or engineering practices that outlasted your tenure at the organisations where you worked.
• Comfortable working closely with distributed teams across geographies and time zones.
• Experience with AI/ML systems in production: LLM integrations, RAG pipelines, or agent architectures.