Technical Support Engineer - West Coast

About Monte Carlo Monte Carlo is the agent trust platform that unifies data and agent observability to monitor, troubleshoot, and improve production AI systems. As enterprises prepare to deploy thousands of agents across business‑critical use cases, Monte Carlo provides the reliability infrastructure to support them along this AI transformation, from human‑guided agents to fully autonomous operations. Founded in 2019 and backed by leading investors, Monte Carlo empowers data and AI teams to ship trusted AI at scale. Learn more at montecarlodata.com. The Role Monte Carlo is hiring Technical Support Engineers to own the end‑to‑end customer experience when things go wrong — from the first Slack message to closing the loop with Engineering. This is not a ticket‑routing function. You’ll dig into customer data stacks, reproduce issues in complex environments, write internal runbooks, and ship fixes to production as a regular part of the job — not an exception. You’ll be joining at a moment when the support function is being rebuilt with AI tooling and proper engineering rigor — which means you’ll have real input into how this team operates. Location: US West Coast (Pacific time zone). This role works closely with West Coast customers and partners — PT hours are required. What You’ll Do Diagnose and resolve technical issues across Monte Carlo’s platform — data pipelines, monitors, alerts, integrations, and agent observability features — using logs, SQL, APIs, and whatever it takes. Own issues end‑to‑end: triage, reproduce, expedite to Engineering when needed, validate fixes, and close the loop with customers. Build and maintain documentation, runbooks, and a knowledge base that actually reduces ticket volume over time. Work alongside the team building AI‑powered support tooling — contribute to prompt design, test coverage, and escalation logic for the bot handling tier‑1 setup and FAQ. Partner with Engineering and Product on bugs and feature gaps — you’re the person who can say “I’ve seen this five times this week” with receipts. Drive high‑priority customer issues over the line — own the coordination across Engineering, CS, and the customer, keep everyone aligned, and don’t let urgency get lost in someone else’s backlog. Collaborate with Customer Success, Sales, and Field Engineering to ensure customer issues don’t fall into gaps between teams. Use AI to surface patterns across cases and bring them to Engineering and Product with data — then build or contribute to the automation that handles those patterns so the team can focus on the complex ones. What We’re Looking For Technical Depth — 2+ years in a technical support, solutions engineering, or SRE‑adjacent role. Comfortable reading logs, writing SQL, using Postman, and navigating cloud environments (AWS, GCP, Azure). Codebase Fluency — Comfortable finding your way around a Python repo: reading PRs, writing fixes, running tests. You don’t need to be a full‑stack engineer, but you should be able to ship a patch. Data Stack Fluency — You know the modern data stack well enough to hold your own: Snowflake, Databricks, BigQuery, dbt, Airflow, or similar. Customers run complex pipelines and you’ll need to understand what’s happening. AI‑Fluent — You understand how AI agents and ML‑driven systems can fail. You’re not intimidated by probabilistic outputs, model drift, or “it worked yesterday.” You’ve used AI coding assistants and LLM tools actively in your workflow — to write runbooks, debug faster, draft responses, or prototype automations — not just experimented once. Bonus: you’ve contributed to or tested AI‑powered support tooling. Customer Communication — Clear, calm, and honest under pressure. You can explain something technically complex to a data engineer and to a VP of Data in the same ticket. Builder Mentality — You write docs without being asked. You notice when a process is broken and propose a fix. You’d rather use AI to automate a repetitive support task than do it manually three more times — and you have examples of doing exactly that. This Is Not For You If You need a well‑defined playbook before you can start — we’re still writing it. You see documentation as overhead rather than part of the job. You prefer structure and clear escalation paths over owning issues end‑to‑end. You want a role where the interesting technical challenges live elsewhere. Why Monte Carlo End‑to‑end ownership — you’ll actually close issues, not just route them. AI support tooling — you’ll contribute to building an AI‑assisted support function, not just use someone else’s bot. Roadmap influence — your case patterns directly feed product and engineering priorities. Equality is a core tenet of Monte Carlo’s culture. We are committed to building an inclusive global team that represents a variety of backgrounds, perspectives, beliefs, and experiences. Monte Carlo is an equal‑opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. #J-18808-Ljbffr Cacheflow