Principal Software Engineer-Platforms

Arcadia is a healthcare technology company whose platform transforms complex healthcare data into a unified foundation for improving care, revenue, and costs. The Principal Software Engineer will own cross-team and platform-level initiatives, drive architectural and operational excellence, lead AI-native engineering adoption, engage customers and partners, and mentor Senior Engineers.

Responsibilities:

• Owning complete vertical slices of cross-team or platform-level work — including the multi-quarter migrations, deprecations, and architectural pivots that single-team scope can't carry
• Setting the technical bar for what "right" looks like in your domain — sharpening designs, catching architectural drift (including in AI-generated work), and shaping the patterns the team builds toward
• Proactively identifying and evaluating platform-level opportunities — new architectures, tools, or capabilities that improve reliability, customer outcomes, or what we can credibly take to market — and driving them to a decision with evidence, alternatives, and tradeoffs
• Defining SLOs and observability for what you ship; sharing on-call ownership as a baseline expectation, not an escalation path
• Leading the rearchitecture of legacy or high-debt areas where the right call is structural — and seeing the migration through to deprecation
• Driving the team's adoption of agentic AI-assisted development — building internal tooling, defining workflows, and setting the standard for what AI-native engineering looks like at Arcadia
• Engaging directly with customers and partners during incidents, design reviews, and major rollouts — closing the loop between what we build and what they actually experience
• Mentoring Senior Engineers through code review, design partnership, and direct coaching — the trajectory of the engineers you coach is part of how your impact is measured
• Influencing engineering practice across teams — architecture standards, operational excellence, AI strategy — through written guidance, working examples, and direct involvement in the hardest problems

Requirements:

• • A track record as the technical owner of cross-team or platform-level initiatives — multiple systems you've taken from ambiguous beginnings through long-term operation, with concrete examples you can walk us through
• • Deep technical foundations across distributed systems, data infrastructure, and service architecture — you reason from first principles, anticipate failure modes, and recognize architectural drift before it compounds. You are the person teammates escalate structural calls to, and the one who catches when an AI-generated design is plausible but wrong
• • Proven ability to own complex systems from design through rollout and long-term operation — including the unglamorous middle (observability, on-call, migration paths, deprecations)
• • A track record of identifying and driving non-obvious technical investments — improvements to the stack, reliability, or platform capability that you spotted, evaluated, and shepherded through the decision
• • Demonstrable depth with agentic AI-assisted development — actively using agentic coding tools, context-engineered environments, and AI-augmented workflows, with examples of AI-native tooling, custom agents, or team patterns you've built, shipped, or substantially shaped. The work needn't have happened at your current employer — what matters is that the depth is real, the engineering judgment driving it is yours, and you can walk us through both in detail, including specific cases where you caught the agent producing plausible-but-wrong work and corrected course
• • Track record of mentoring and coaching Senior Engineers to higher levels of judgment and impact — not just answering questions, but visibly raising their bar over time
• • Strong customer and product orientation — comfortable engaging directly with customers, partners, or downstream consumers to define problems before building and validate outcomes after shipping
• • Clear written communication — design docs, reviews, and post-release writeups that establish standards beyond your immediate work
• • Demonstrated influence across team boundaries through technical credibility rather than positional authority
• • Experience defining org-wide AI adoption strategy — what was adopted, what was rejected, what got measured, and what the org actually changed about how it builds
• • Healthcare technology, HIPAA-regulated environments, or other regulated data domain experience
• • Track record of incubating new product directions or greenfield platform capabilities from ambiguous starting points
• • Speaking, writing, or open-source presence that reflects broader technical influence