Principal Software Engineer

The Phoenix Group; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

The Phoenix Group seeks a seasoned software engineering leader who writes code, mentors engineers, and shapes the standards, tooling, and SDLC practices that power high‑quality software delivery. You will bridge traditional development discipline with AI‑assisted workflows to keep code safe and maintainable across teams.

In the near term you will be hands‑on: define coding, review, testing, and shipping standards; select internal developer tooling; and cultivate a culture of quality.

Qualifications

• Track record leading software engineering teams.
• Experience building or owning an internal developer platform as a software product.
• Familiarity with developer productivity metrics (cycle time, PR quality, DORA metrics, test coverage).
• Experience driving engineering standards adoption across an organization.
• Hands-on experience with AI-assisted development tools and workflows.

Responsibilities

• Define how the organization writes, reviews, tests, and ships code; set hands‑on standards.
• Build or select the internal developer platform and tooling; establish engineering culture and norms.
• Drive adoption of standards across teams in both human‑written and AI-assisted contexts.

Skills

Software engineering leadership

Internal developer platform

CI/CD tooling

AI-assisted development

Code quality

Tools

Internal developer platform

CI/CD tooling

Code review tooling

Job description

This is a role for someone who has spent their career writing code, leading engineers who write code, and thinking deeply about the craft and process behind great software, engineering standards, developer tooling, internal platforms, code quality, SDLC maturity, and developer experience in the IDE-and-PR sense, not the network-and-cluster sense.

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.

What We're Looking For

• 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