Principal Software Engineer

What makes this role distinct is the mandate to operate across two worlds at once: the classical, human-first software engineering discipline (SDLC rigor, code review culture, testing standards, architectural guardrails) and the emerging AI-assisted development landscape (agentic coding, LLM-powered IDEs, vibe coding, prompt-driven prototyping). The ideal candidate respects both deeply and can build the bridge between them without compromising the fundamentals that make AI-generated code safe to ship.

The Role

• Short term, this person will set standards hands-on and stay hands-on themselves: defining how the org writes, reviews, tests, and ships code; building or selecting the internal developer platform and tooling; establishing engineering culture and norms; and driving adoption of those standards across teams, in both human-written and AI-assisted contexts. This is not a purely strategic or oversight role early on, they’ll be in the code, in the reviews, and in the tooling decisions directly.
• Medium-term, the role will evolve into a Head of Software Engineering position with broader organizational ownership as the foundation matures.

• A track record leading software engineering teams specifically (not infrastructure or ops), with strong personal coding roots
• Experience building or owning an internal developer platform as a software product: golden paths, self-service tooling, CI/CD treated as a developer experience problem
• A way of thinking about developer productivity in terms of cycle time, PR quality, DORA metrics, and test coverage, not uptime, cost optimization, or cloud architecture
• Experience driving engineering standards adoption across an organization (coding standards, code review culture, testing practices, architectural guardrails) with the ability to sell it, not just mandate it
• Deep, practical command of classical SDLC, from requirements through design, implementation, testing, deployment, and maintenance, and experience building or enforcing those practices in real engineering orgs
• Hands-on experience navigating the AI-assisted development shift: deploying LLM-powered IDEs (Copilot, Cursor, Claude Code), building agentic coding workflows, and reasoning carefully about where AI accelerates engineering versus where it introduces risk without discipline
• The ability to set standards specific to the AI-assisted era: what a good AI-assisted PR looks like, how to review vibe-coded output, when an LLM-generated prototype is production ready versus a liability, and how to preserve code quality, test coverage, and architectural integrity when a large share of engineers are prompting their way to a first draft
• A genuinely considered, defensible point of view on the human+AI engineering workflow, neither cheerleader nor skeptic, but a practitioner who has thought it through and built real systems around it