Valley Customer Support Engineer (AI-Powered Support)

COMP: $1.5k to $2.5k per month

APPLY HERE: https://app.hiretruffle.com/s1/OVRtLBP1jIYxzmsp9N

ABOUT VALLEY

Valley is an AI-powered outbound platform built around LinkedIn-first prospecting. We help teams find, research, and reach the right buyers, combining ICP targeting, intent signals like profile views and post engagement, and deep prospect research (recent posts, company news, funding, job changes) to generate personalised outreach that turns cold audiences into qualified conversations. Our customers range from self-serve operators running their own campaigns to managed-service clients who hand the entire motion to us.

We move fast, we're data-grounded, and we care about outcomes over activity. The people who thrive here are operators. They diagnose before they execute, they own their numbers, and they'd rather fix the thing blocking results than talk around it.

ABOUT THE SUPPORT FUNCTION

Support at Valley is not a ticket queue with people stacked on top of it. It's a system. We're building a support engine where 1-2 people, armed with AI tooling and automation, deliver the coverage, speed, and quality of a team of 10-20. That means every repetitive question gets deflected or automated, every escalation reaches a human with full context already assembled, and the support system itself gets smarter every week.

You won't just answer tickets. You'll build the machine that answers most of them for you, then handle the hard ones yourself with the time you've freed up.

THE ROLE

The Customer Support Engineer supports/owns Valley's support system end to end: the tooling, the automations, the knowledge base, the escalation paths, and the customer conversations that still need a human. This is an operations-and-engineering-first role: success is about designing workflows, triggers, and AI-assisted resolution paths that scale support without scaling headcount, then using the data those systems generate to fix the product friction causing tickets in the first place.

You'll live inside tools like Pylon, Basedash, Claude Code, n8n, and Zapier. You'll analyse ticket and product data to find patterns, build automations that resolve or route issues before a human touches them, maintain a knowledge base that both customers and AI agents draw from, and personally handle the escalations that automation can't. You are the person who can explain why customers are struggling, what the system is doing about it, and what happens next.

KEY RESPONSIBILITIES

Support system architecture

- Own the support stack end to end: helpdesk configuration (Pylon), AI agent training, routing rules, SLAs, and escalation paths.

- Design and continuously improve the system so that most inbound volume is deflected, auto-resolved, or arrives pre-triaged with full customer context.

- Treat support as a product: ship improvements weekly, measure their impact, iterate.

Automation and workflow building

- Build and maintain automations in n8n and Zapier: triggers, routing workflows, auto-responses, data syncs between support, CRM, and product systems.

- Use Claude Code and AI tooling to build internal scripts, agents, and tools that eliminate repetitive support work.

- Identify every manual, repeated support task and systematically automate it out of existence.

Data analysis and reporting

- Use Basedash and product data to diagnose customer issues at the source: query usage data, spot patterns, and resolve root causes rather than symptoms.

- Track and report the numbers that matter: ticket volume, deflection rate, first-response and resolution times, CSAT, and top ticket drivers.

- Explain the story behind the numbers: why volume is trending the way it is, what's driving it, and what the plan is, not just the metrics.

Knowledge base and AI training

- Own the help centre and internal knowledge base. Keep documentation accurate, current, and structured so AI agents can draw from it reliably.

- Train and refine AI support agents on real ticket data, and QC their responses so automation never degrades quality.

- Turn every novel resolved ticket into documentation or automation so the same question never needs solving twice.

Frontline resolution and escalations

- Personally handle the escalations that automation can't: complex campaign issues, account problems, and technically nuanced questions.

- Reproduce bugs, gather context, and hand engineering clean, well-documented reports they can act on immediately.

- Flag at-risk customers early and route churn signals to the right owner before they become cancellations.

Product feedback loop

- Aggregate support data into clear product feedback: what's confusing users, what's breaking, and what would eliminate the most tickets if fixed.

- Work directly with engineering and product to close the loop, and verify fixes actually reduce volume.

WHAT MAKES SOMEONE SUCCESSFUL HERE

You think like an engineer before a support agent. You're systematic enough to see every ticket as a pattern rather than an incident, technical enough to build the automation that handles it, and customer-obsessed enough to make the human touchpoints that remain genuinely excellent.

- Proficiency with AI tooling and automation platforms: Pylon (or similar helpdesks), Basedash, Claude Code, n8n, Zapier, and the judgment to know which tool fits which problem.

- Strong data analysis skills: comfortable querying data, reading dashboards, and diagnosing issues from usage patterns rather than guesswork.

- Ability to build workflows, triggers, and integrations hands-on, not just spec them for someone else.

- Strong writing: clear, fast, empathetic customer communication and documentation that both humans and AI agents can use.

- Genuine ownership of support outcomes, with the discipline to work root causes before adding headcount or band-aids.

- Early churn-radar: you spot at-risk accounts in support signals before they become cancellations.

NICE TO HAVE

- Prior experience in technical support, support engineering, or support ops at a SaaS company.

- Experience training or deploying AI support agents (chatbots, auto-resolution, AI-drafted replies).

- Familiarity with LinkedIn outbound, B2B sales tooling, or the GTM software space.

- Light scripting ability (Python, JavaScript, SQL) for building internal tools and queries.