{"id":3437285,"url":"https://alion.io/job/unevenlabs-internal-tooling-engineer","title":"Internal Tooling Engineer","company":{"id":4476545,"name":"Uneven Labs","domain":"unevenlabs.com","url":"https://alion.io/company/unevenlabs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Kula","truth_index":null},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":85000,"max_usd":170000,"period":"year","method":"global_role_seniority_cell","sample_n":8444},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Datadog","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Platform Engineering","optional":false},{"name":"TypeScript","optional":false},{"name":"Model Context Protocol","optional":true},{"name":"Rust","optional":true},{"name":"Slack","optional":true}],"status":"live","first_seen_at":"2026-10-28T03:39:20Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-01T02:20:51Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About us\nRelay is a cross-chain payments protocol that provides instant, low-cost cross-chain bridging, swaps, and transactions. We're the fastest and cheapest way to bridge and transact across chains, serving over 5 million users who have completed 59+ million transactions with $6+ billion in volume across 85+ chains.\nOur mission is to make transacting across chains as fast, cheap, and reliable as online payments. We are building the core infrastructure to abstract chains from end user payments, enabling the next billion users to experience the benefits of blockchain without the UX burdens.\nAbout the role\nWe're hiring an Internal Tooling Engineer with one goal: make every Relay engineer a lot more productive.\nYou'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. Think of the role as a forward-deployed engineer for our engineering org: you find where people wait, get stuck, or repeat themselves, and you remove it.\nIn 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, and 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.\nWhat you'll do\nImprove our agentic development workflows\nWork with our internal AI agent, Scout, and the teams that use it, to turn repetitive engineering work into agent skills: investigation, triage, ticket routing, status updates, and first-draft fixes\n\nOwn the shared library of skills, tools, and context that engineers' coding agents rely on, so what works for one team works for all, whether they use Claude, Codex or any other flavor of a coding agent.\n\nWatch how engineers actually work with AI tools, find where they get stuck, and fix the workflow, the tooling, or the guidance\n\nTrack cycle time, token cost, and rework, and report what each change moved\n\nMake agents the first reviewer of all code\nPut in place mechanisms so agents review and potentially approve (or request changes!) every PR, and engineers are never blocked waiting for another human's review.\n\nClose the loop on every review: capture each human override, missed bug (false negative), and wrong or dismissed comment (false positive), and feed them back into evals, prompts, and rules, so the agent approves more and gets it wrong less over time\n\nBuild the surrounding safety net: tests, deploy guards, and rollback, so faster review does not mean riskier releases\n\nTrack false-positive and false-negative rates over time, and widen what the agent may approve on its own only as those rates fall\n\nMake debugging issues trivial\nWork with tools like Datadog so the logs, traces, metrics, and recent deploys behind any issue are one query, or one agent question, away\n\nImprove instrumentation and alert ownership so every alarm points to a team and a likely cause\n\nCut observability noise and cost without losing the signals engineers need\n\nTurn recurring incident patterns into runbooks and automation that Scout can run\n\nAbout you\nYou're a hands-on engineer who ships tools other engineers use every day, and you treat them as your customers\n\nYou're obsessed with cycle time: you notice every minute someone waits, and you find out why\n\nYou use AI coding agents heavily yourself and know what makes them reliable or flaky\n\nYou'd rather add a test, a guardrail, or a lint rule than rely on a prompt\n\nYou know the difference between an AI review comment engineers act on and one they learn to ignore\n\nYou'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\n\nYou build feedback loops: you measure before and after, learn from every miss, iterate, and report honestly when something didn't help\n\nSkills and abilities\nMust-have\nHands-on backend engineering experience: you build, deploy, and maintain production services yourself, including instrumentation, logging, monitoring, and alerting\n\n5+ years in software engineering, developer tooling, or platform engineering\n\nStrong TypeScript and experience building internal tools and API integrations\n\nProduction experience with LLM agents: tool use, retrieval, orchestration, and evals for nondeterministic behavior\n\nExperience 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\n\nWorking knowledge of CI/CD and code review systems, and what makes them slow\n\nExperience with observability tools such as Datadog: logs, traces, metrics, monitors, and cost control\n\nStrong written communication; you can turn an engineer's pain point into a precise spec\n\nNice to have\nExperience with Rust (we use a little of it)\n\nExperience with MCP or comparable tool and context protocols\n\nExperience designing risk-based merge policies, deploy guards, or automated approval flows\n\nExperience with incident tooling, on-call workflows, or runbook automation\n\nExperience with Slack apps or internal platforms\n\nExperience in crypto, payments, or another high-stakes domain\n\nWhat success looks like\nFirst 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\n\nFirst 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\n\nFirst 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\n\nOngoing: 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\n\nWhat we offer\nCompetitive base salary\n\nEquity package\n\nComprehensive health benefits\n\nUnlimited PTO policy with encouraged minimum\n\nRemote-first culture with emphasis on collaboration, communication, and 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