RB Global
From RB Global's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
We're hiring the founding engineers for this team.
Senior Software Engineer, AI Enablement
RB Global is building the team that makes agentic software development standard, governed, and measurable across the company. We want every engineer to be able to delegate work to an agent and trust the result, with the right standards, guardrails, and visibility built into the path itself. This new team exists to make that the default: composing what already exists wherever it's good enough, and building only what doesn't. It's engineering excellence applied specifically to how we build software with AI.
We're hiring the founding engineers for this team. You'll help shape the team's scope, standards, and first deliverables from day one, not inherit someone else's roadmap.
Help build secure, governed ways for agents to reach internal tools and data, with the scoping, credentials, and audit trails that make that access trustworthy.
Help build and run the containerized environments agents work in, where things like isolation, reproducibility, and model/cost choices are actually controlled.
Help turn the team's engineering standards and conventions into something tools and agents can actually consume and follow, not just documentation that drifts out of date.
Contribute to establishing the baselines and metrics the team doesn't have yet, so claims about speed, quality, and cost are backed by numbers.
Work with CI/CD, security, and platform teams to plug agent workflows into tools the company already uses, rather than reinventing them.
Help review and harden what the team ships with the same rigor you'd apply to any system other engineers depend on: tests, documentation, and a clear plan for who supports it.
Share your perspective with teammates on what the team takes on, and when to adopt an existing tool versus build something new.
Work with the team's technical product owner and stakeholders to understand priorities and turn ideas into delivered work.
Work closely with the other engineers on the team to reason through ambiguous problems together. We want your questions, ideas, and pushback, not just completed tasks.
Solid professional software engineering experience at a senior level (5+ years of professional experience), with a record of delivering reliable work and speaking up to improve how the team approaches it.
Familiarity with internal developer platforms, "paved road" tooling, or developer-enablement work, and an interest in making other engineers more effective.
Hands-on experience with CI/CD pipelines, including quality gates defined as code.
Experience with Kubernetes or comparable container orchestration, and comfort supporting services from build through production operation.
Working knowledge of observability, such as structured logging, tracing, and metrics/dashboards (e.g., Datadog, OpenTelemetry, or similar), and using it to answer "is this actually working?"
Comfort working in Python; we care more about your ability to reason across a stack and pick up what's needed than mastery of any one language.
Curiosity and a genuine desire to learn: this space is moving fast, the right answers aren't settled yet, and the best candidates will enjoy figuring them out rather than expecting a playbook. This is essential for the role.
A habit of contributing to the discussion: you ask questions, offer ideas, and share your view, rather than waiting to be handed a task.
Strong communication skills, with the ability to explain technical trade-offs clearly to teammates, other engineering teams, and stakeholders who weren't in the room when the decision was made.
Comfort working through ambiguous or evolving problems with teammates, since much of this team's early work won't have a settled playbook to follow.
Nice To Have
Experience using AI-assisted development tools (e.g., GitHub Copilot, agentic coding tools), and even better, experience building platforms, tooling, or guardrails around them.
Experience building AI tooling, personal or professional, and a natural enjoyment of experimenting with new approaches.
Familiarity with MCP (Model Context Protocol) or similar tool-access and governance patterns.
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