[Hiring] Internal Tooling Engineer @Relay

Role Description

We're hiring an Internal Tooling Engineer with one goal: make every Relay engineer a lot more productive. You'll work "inside the box". Your customers are our own engineers, and the things you build never ship to end users but decide how fast and how safely everything else ships.

In the short term, this is an individual contributor, hands-on backend engineering role. You (or your agents) will write code every day, deploy it, and run it in production:

• Building services and integrations
• Instrumenting them
• Setting up monitoring and alerts
• Being on call for what you ship

There is no team to manage. If you want to lead people or mostly advise, this isn't the role.

Qualifications

• Hands-on backend engineering experience: you build, deploy, and maintain production services yourself, including instrumentation, logging, monitoring, and alerting
• 5+ years in software engineering, developer tooling, or platform engineering
• Strong TypeScript and experience building internal tools and API integrations
• Production experience with LLM agents: tool use, retrieval, orchestration, and evals for nondeterministic behavior
• Experience bringing AI-assisted code review or testing into a team's daily workflow, measuring the result, and improving it through feedback loops that reduce false positives and false negatives
• Working knowledge of CI/CD and code review systems, and what makes them slow
• Experience with observability tools such as Datadog: logs, traces, metrics, monitors, and cost control
• Strong written communication; you can turn an engineer's pain point into a precise spec

Requirements

• You're a hands-on engineer who ships tools other engineers use every day, and you treat them as your customers
• You're obsessed with cycle time: you notice every minute someone waits, and you find out why
• You use AI coding agents heavily yourself and know what makes them reliable or flaky
• You'd rather add a test, a guardrail, or a lint rule than rely on a prompt
• You know the difference between an AI review comment engineers act on and one they learn to ignore
• You're an IC who's comfortable being the first dedicated hire in a new function, and who works well with people who are busy and skeptical
• You build feedback loops: you measure before and after, learn from every miss, iterate, and report honestly when something didn't help

Benefits

• Competitive base salary
• Equity package
• Comprehensive health benefits
• Unlimited PTO policy with encouraged minimum
• Remote-first culture with emphasis on collaboration, communication, and flexibility

What success looks like

• First 30 days: you've shadowed engineers across teams, mapped where time is lost from commit to production, and agreed a baseline for cycle time and review wait
• First 60 days: agent review covers a first set of repositories, with clear rules for what merges without a human and a feedback loop capturing every override and miss
• First 90 days: median wait for a first review is measured in minutes, the review agent's false-positive and false-negative rates are tracked and falling, and a Datadog-backed debugging flow is in daily use
• Ongoing: engineers ship visibly faster, and the review agent keeps improving: it approves more on its own, with fewer false positives and false negatives each month