Customer Operations AI Engineer

Tread is an AI-native vertical SaaS platform transforming construction materials logistics. The company crossed $1Bn in monthly delivered load value in March 2026. The platform serves Haulers, Producers, and Contractors, optimizing truck routing and delivery while providing customer service through AI agents. Tread is Series A-funded by Mucker Capital.

Why This Role Exists

Tread's customers operate complex, high-stakes logistics, and you can't onboard, support, or grow them from a desk. This is the customer-side counterpart to our product-side Forward Deployed Engineer: instead of shipping product code, you build and own the AI agents that run support, onboarding, and success, deployed alongside real customers with feedback loops measured in hours. As AI takes on the volume of customer work, this role lets us scale customer operations without scaling headcount. It sits in Customer Operations, reports to the VP of Customer Ops, and is measured on customer outcomes.

What You Own

• Agentic Support: Build the agents that resolve support end-to-end. Anything AI can answer gets answered by AI, with a human audit. First targets are our two highest-volume drivers: login issues and multi-driver phone-number issues. Own the AI support layer (Fin+ the Mintlify knowledge base behind it): decide what's safe to automate, validate against real prior conversations before go-live, and keep the content trustworthy.

• Agentic Onboarding: Build end-to-end agentic onboarding flow, customized per customer. Vendor onboarding comes first, then product onboarding and case reporting. The agent tracks each customer's progress, delivers the right step at the right moment, and escalates only the stalls.

• Agentic Success & Enablement: Map every post-onboarding job to be done (QBRs, monthly check-ins, renewal prep) and automate it, including voice-based check-ins. Build account-health and usage monitoring that flags risk before a human would see it, plus self-serve enablement that reduces hands-on support.

• Expansion & Retention Through Technical Depth: Solve customer problems preemptively, surface stickiness factors, and turn every recurring issue into a reusable agent so it never comes back to a human.

What Success Looks Like

First 90 Days

• Visit at least 3 customer sites and describe their workflows from memory

• Ship your first support automation against login + multi-driver phone-number issues

• Get Fin validated and live on a cleaned-up knowledge base

• Take ownership of onboarding for 1 new customer, run agentically end-to-end

• Establish a baseline view of where human effort is being spent across support, onboarding, and success

First 6 Months

• One end-to-end vendor-onboarding flow running agentically, with a human only on exceptions

• Post-onboarding success jobs mapped and the first check-in / health agent live

• Customer signals turn into shipped agent improvements in under a week

• Time-to-first-value trending measurably downward

First Year

• A rising, defensible line on % of support, onboarding, and success work handled by agents

• Customers in your portfolio show measurably higher adoption and satisfaction

• You've defined the Customer-Ops FDE playbook for future hires

• You're the trusted person customers rely on for the problems agents can't solve

What We're Looking For

• AI-native builder. You automate your job away by instinct and reach for agents first. You're dangerous with APIs, scripts, SQL, prompts, and agent frameworks. You ship working systems without waiting on a full software engineer.

• Strong technical judgment. You can look at agent output or a dataset and know what's wrong, what's risky, and what's safe to release. Auditing AI is a core skill of this job.

• Customer-facing instincts. You build trust with operations leaders and are comfortable owning a QBR with a CTO and a scrappy save with a dispatcher in the same day.

• Bias toward action without waiting for tickets; high ownership and low ego.

• Strong communication. You translate messy customer conversations into clear problem statements a system can solve.

• Willingness to travel occasionally for on-site customer work.

• Analytical mindset. You instrument what you ship and let data decide the next step.

Bonus Points

• Construction technology, heavy civil, logistics, supply chain, or field-services experience

• Prior support, implementation, CS, or solutions role you made dramatically more efficient with automation

• Experience building with LLMs / AI agents in real customer workflows

• Familiarity with our stack surfaces: Intercom, Linear, Omni, HubSpot, PostHog, and import/REST APIs

• Series A–C startup experience under 50 people in ambiguous environments

Compensation & Location:

The anticipated base salary range for this role is $120,000–$160,000. Final compensation will be determined based on factors including experience, skills, and geographic location.

While our preference is for candidates based in San Francisco, we are open to exceptional remote candidates within the United States.