Principal Software Engineer

Grubhub•Chicago, IL

4d•$244,000 - $253,500

About The Position

Wonder is hiring a Principal Engineer to set the technical direction for a new team and bring AI-enabled development practices to Wonder Engineering. The role defines architecture and guides delivery of solutions that address context fragmentation, MCPs and CLIs, skills, sub-agents, and integrations connecting AI tooling to Wonder’s codebases and systems. It also shapes AI-assisted workflows for code generation, review, testing, documentation, refactoring, and on-call and incident management. The Principal Engineer will bring proven AI-tooling developer patterns and practices from outside Wonder, and work closely with the team’s engineers, senior engineering leadership, and the platform, infrastructure, and application teams whose workflows the solutions support. This includes setting direction for autonomous development agents and cloud agent solutions, with appropriate access controls and guardrails; establishing standards for AI-assisted development; optimizing AI costs and model selection; and measuring developer velocity in relation to AI spend and outcomes. The position provides technical direction across teams, helps ensure deliverables form a complete cross-team solution, and weighs tradeoffs such as build versus buy, cost, and risk. It also requires clear strategy documents, specifications, and documentation; effective communication of technical tradeoffs across departments and leadership levels; and mentorship for senior engineers developing durable, auditable AI-assisted engineering practices.

Requirements

Bachelor’s or Master’s degree in Computer Science or a related field, or equivalent practical experience.
10+ years of professional software engineering experience, including recent hands-on work building with or on top of LLM-based tooling such as agents, MCP servers, or coding assistants.
Direct experience integrating AI tools into real development workflows—such as code review, testing, code generation, or documentation—beyond individual end-user use.
Strong system design skills and a track record of owning tools or platforms end-to-end, from design through adoption across multiple teams.
Comfort operating with ambiguity and shaping practices for a new team without established playbooks.
Experience designing and consuming APIs, webhooks, or other integration/message-based systems.
Experience building developer tooling, internal platforms, CI/CD systems, or other software primarily used by engineers.
Pragmatic problem-solving skills and ability to troubleshoot complex technical obstacles.
Excellent communication and interpersonal skills, including ability to drive technical discussions with engineers and leaders across multiple teams up to the senior director level.

Nice To Haves

Experience with Java/JVM and/or Python.
Experience integrating AI tools into development workflows at organizational scale.

Responsibilities

Define architecture and guide delivery of context aggregation solutions, MCPs/CLIs, skills, and sub-agents that provide AI coding tools sanctioned, reliable access to Wonder’s codebases, services, and systems.
Set direction for autonomous development agents and cloud agent solutions, including access controls and guardrails.
Identify effective uses of AI-assisted development in code review, testing, code generation, and documentation, and translate emerging practices into standards suitable for Wonder’s stack, scale, and constraints.
Set strategy for pre-review and triage tooling that automates mechanical and policy issues before human review, and define standards for reviewing AI-generated code.
Define strategy and architecture for optimizing organization-wide AI costs and abstracting model selection from engineers’ day-to-day work.
Define and measure developer velocity, connecting AI spend to the outcomes it produces.
Set technical direction for developer tooling and coordinate deliverables across teams to create complete cross-team solutions.
Weigh technical tradeoffs, including build versus buy, cost, and risk, and partner with platform, infrastructure, application teams, and engineering leadership to deliver solutions to production.
Write strategy documents, specifications, and documentation; provide, seek, and incorporate feedback.
Translate complex technical tradeoffs for audiences across departments and leadership levels.
Mentor senior engineers in building durable, auditable AI-assisted engineering practices.