Technical Support Engineer - West Coast

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.

- 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, escalate 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

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.

- 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

- 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