Software Engineer—Product Development

AAPC•Salt Lake City, UT

1d•Remote

About The Position

AAPC seeks a distinctive type of software engineer: someone who blends strong technical skills with curiosity, initiative, product insight, and a rare passion for delivering value. Your role will involve building and enhancing our platforms and products. You will collaborate with AAPC’s Technology and Engineering team, working closely with Product in a small, empowered group. The Product team will clarify the customer problem, its importance, and the business rules that guide the solution. You will be responsible for translating that guidance into functional technology that reaches users and delivers measurable benefits. We want engineers who can move from understanding the “what” and “why” to figuring out the “how.” Analyze the problem, manage ambiguity, make sound technical choices, and execute effectively. You need to work quickly, exercise judgment, and own the process end to end. Modern tooling has fundamentally changed what an exceptional engineer can accomplish. In this role, you will treat AI as an extension of your engineering team. By leveraging agentic coding tools, reusable skills, agentic engineering practices, established components, and internal frameworks, you will orchestrate work that historically required a much larger team.

Requirements

At least a few years of professional software development experience.
Demonstrated experience taking software from an initial idea through production delivery.
Practical experience across the broader technology lifecycle, including software development, deployment, DevOps, observability, monitoring, and production support.
The ability to work across technical boundaries rather than remaining limited to one narrow layer of a system.
Strong judgment about when to act independently, when to investigate further, and when a decision must return to Product or another responsible stakeholder.
Experience using agentic coding tools, or compelling evidence that you are actively developing an agentic engineering practice.
The ability to direct, evaluate, and integrate AI-generated work while retaining full accountability for its correctness and quality.
A history of learning unfamiliar technologies and applying them to real problems.
Clear, concise communication, especially when raising questions, explaining tradeoffs, or working asynchronously.
A customer-oriented mindset and an understanding that successful engineering creates customer and business value.
The initiative to improve the systems and processes around you rather than repeatedly working around the same problems.
We value demonstrated capability, judgment, and personal drive more than a particular title or traditional seniority level. Successful candidates may come from different career stages, but they will share the ability to own work from idea to production.

Responsibilities

Turn clearly articulated customer problems, business needs, and business rules into production-ready technology.
Own delivery across the complete technology lifecycle: discovery, technical design, implementation, testing, deployment, observability, monitoring, support, and continuous improvement.
Break complex outcomes into executable work and coordinate agents toward a common goal.
Investigate and resolve ambiguity independently when you can determine the answer through technical judgment, research, customer context, or existing standards.
Identify genuine product questions early and raise them quickly enough that answers don’t delay delivery
Make technical decisions autonomously while remaining aligned with shared architectural principles, platforms, and standards.
Deliver valuable increments early, learn from real usage, and iterate rapidly.
Create and improve reusable skills, components, frameworks, workflows, and automation that increase the effectiveness of future engineering work.
Strengthen testing, code review, security analysis, accessibility, quality assurance, monitoring, and incident diagnosis.
Measure success by customer and business outcomes, not by the number of stories completed or the amount of code produced.