AI-Native Technical Support Engineer

Trucker Path Inc.; Phoenix (AZ); On-site; USD 60,000 - 80,000; Full time

Job summary

A leading logistics technology company in Phoenix, AZ is seeking a technical support specialist to manage AI-native support for fleet customers. This role focuses on quickly diagnosing issues and enhancing customer experience through clear communication and effective troubleshooting. Ideal candidates will have 1-3 years in technical support and be proficient in using AI tools. Strong written English and an understanding of APIs are essential. The company offers a dynamic work environment with opportunities for growth.

Qualifications

• 1–3+ years in technical support or similar roles.
• Strong troubleshooting skills across logs and error messages.
• AI-forward working style and excellent customer communication.

Responsibilities

• Operate AI-native support workflows to resolve customer issues.
• Diagnose and reproduce technical problems for fleet customers.
• Create knowledge bases and internal documentation to speed up issue resolution.

Skills

Technical troubleshooting

Customer communication

Use of AI tools

Understanding of APIs

Networking basics

Education

Experience in technical support or similar

Job description

Description

Overview

Trucker Path is a leading mobile platform built specifically for professional truck drivers and fleets. It offers truck-safe GPS navigation, real-time parking availability, weigh station status, fuel prices, and route planning tools. The platform helps drivers operate more efficiently, reduce costs, and stay compliant on the road. NavPro enhances large fleet performance with advanced tracking and compliance tools, providing greater visibility and control across operations.

Role purpose

Own NavPro’s AI-native front-line technical support and escalation quality for fleet customers: resolve issues quickly, improve customer experience, and create a tight feedback loop from real-world incidents ? root cause ? product/engineering action ? verified improvement.

Key responsibilities

• Operate AI-native support workflows: Use AI to draft customer updates, internal incident summaries, and escalation notes that are accurate and action-oriented.
• Operate AI-native support workflows: Use AI to propose likely causes, next-best tests, and targeted data requests, then validate with evidence.
• Operate AI-native support workflows: Build lightweight automations (macros, scripts, dashboards) to reduce repetitive work and improve resolution speed.
• Operate AI-native support workflows: Maintain high signal-to-noise documentation (what happened, why, what we learned, what we changed).
• Handle fleet customer technical issues: Diagnose problems, reproduce bugs, and resolve issues directly when possible.
• Handle fleet customer technical issues: Guide users through troubleshooting steps with clear, timely updates.
• Escalate effectively to Tier 2 engineering: Escalate unresolved issues with complete documentation.
• Escalate effectively to Tier 2 engineering: Include repro steps, logs, screenshots, and customer context to enable fast resolution.
• Convert recurring issues into product and technical solutions (AI-native): Use AI to summarize tickets, cluster themes, and surface the highest-impact patterns.
• Convert recurring issues into product and technical solutions (AI-native): Run structured investigations (repro steps, logs, request traces, data checks, environment diffs).
• Convert recurring issues into product and technical solutions (AI-native): Write crisp problem statements, root-cause hypotheses, and proposed fixes or workarounds.
• Convert recurring issues into product and technical solutions (AI-native): Define acceptance criteria, rollout plans, and success metrics.
• Convert recurring issues into product and technical solutions (AI-native): Partner with Product and Engineering to drive items onto the roadmap, ship fixes, and validate post-release.
• Build and maintain support knowledge and tooling: Maintain a knowledge base of common issues and solutions.
• Build and maintain support knowledge and tooling: Create internal runbooks and troubleshooting checklists.
• Build and maintain support knowledge and tooling: Identify automation opportunities for deflection and faster resolution.
• Track and improve support performance: Track response time, resolution time, and escalation rate.
• Track and improve support performance: Identify top drivers of volume and drive measurable improvements.

Requirements

• 1–3+ years in technical support, customer support engineering, implementation support, or similar technical customer-facing roles.
• Strong troubleshooting skills across logs, error messages, networking basics, APIs, system configuration, and debugging mobile apps and web portals.
• AI-forward working style: uses AI assistants daily for synthesis, investigation, writing, and iteration.
• Excellent customer communication skills and ability to explain technical concepts clearly.
• Excellent written English for technical documentation and customer correspondence.
• Preferred: B2B enterprise support experience, familiarity with fleet or logistics software, and experience supporting production mobile applications and web portals.
• Note: The original text uses tags; in this refinement they are treated as bold via per instructions.

What success looks like (first 90 days)

• Faster first response and time-to-resolution for top fleet issue categories.
• Higher-quality escalations (complete repros, logs, and clear hypotheses), reducing engineering back-and-forth.
• A living knowledge base and runbooks that deflect repeat issues and speed up onboarding.
• A steady pipeline of well-formed product feedback tied to measurable impact (volume, severity, churn risk).