Founding CS Engineer

Lightfield•San Francisco, CA

2d•$170,000 - $195,000•Hybrid

About The Position

Lightfield is an AI-native CRM that assembles itself from your email, calendar, and meetings. It captures every interaction and turns it into organized context: accounts, tasks, follow-ups, and insights, so nothing slips through the cracks. We’re rethinking CRM from first principles. Instead of forcing teams to maintain rigid systems, Lightfield learns from how companies actually work, adapting, automating, and surfacing the insight that drives growth. We’re building the CRM platform we always wished existed: fast, intelligent, and genuinely helpful. We are backed by Greylock, Lightspeed, and Coatue, and our founders previously built Tome, a generative AI presentation product used by over 25 million people. Before Lightfield, our team worked on Llama, Instagram, Facebook Messenger, Pinterest, Google, and Salesforce. We've shipped a product founders love. Nearly 2,000 companies have signed up since our November launch. And our customers build inside Lightfield: writing skills, designing automations, wiring our workflows into how they operate. That changes what support has to be. Every Support and Customer Success team in software wants the same thing: keep customers successful as they scale, without adding a body for every cohort. Most can't get there. They bolt AI onto tools built for manual work, and the seams show. Lightfield starts from the other end. The product is the AI engine, the same one our customers use to run their own go-to-market. So we can build what almost no one can: a post-sales function that's AI-native from the ground up, running on the product itself. We're hiring our first CS Engineer to build it. In the near term, you own Support and Customer Success outright. You're in the queue every day handling real tickets, and you build the systems that make it better: Health scoring that flags risk before a renewal slips Onboarding that runs itself A front-line agent that handles the first wave of questions A knowledge base as the single source of truth, written for AI agents first Every issue you resolve teaches the system, so the next one is easier. You scale through systems, not headcount. Support is also the front edge of the customer experience. Done well, it's why customers stay and expand, which puts this role closer to revenue than a typical support seat. The bigger prize is the function itself. You define what AI-native post-sales looks like in production. Get it right and it becomes the model Lightfield scales on. This is a founding individual-contributor role. You own the work yourself today, with a clear path to leading the function and its team as it grows. You report to the Head of Customer Success and work with engineering, product, and customers every day.

Requirements

This role is hands-on. You live in the support queue and build the systems that make it smaller. The best person for it wants both.
A technical, customer-facing background: support engineering, solutions engineering, CS Ops, or technical consulting. The reps matter more than the years
You're good in the weeds. You'll handle real ticket volume yourself, from workflow debugging to integration issues, and you like that part of the job
You build as you go. You turn recurring issues into docs, automation, and tooling, so the queue gets lighter over time
Technical depth: you write scripts, hit APIs, and debug integrations without waiting on engineering
You're comfortable on a call with a technical founder, and you pick up new tools fast

Nice To Haves

Bonus: you've built on LLMs and AI agents, or owned support solo before

Responsibilities

Own support, end to end
Own the inbound queue across Slack and email, including response time and resolution quality
Set and hold SLAs, triage, and define escalation criteria
Resolve technical tickets yourself: workflow debugging, integration issues, agent behavior, custom object setup
Turn recurring tickets into documentation
Build the system that scales it
Stand up the AI support agent and drive deflection up
Keep the knowledge base ahead of the product, so the agent answers without you
Build internal tooling for what customers can't self-serve yet. Engineering absorbs it over time, so you build what's needed now
Build the health score model around a north-star metric that predicts retention and expansion
Drive activation and retention
Deploy automated onboarding for every new customer: in-product education, activation nudges, time-to-value sequences
Run proactive rescues on at-risk accounts
Surface expansion signals. With consumption-based pricing, this is a revenue function
Document it and close the loop
Write the Support and CS playbook: SOPs, triggers, rules of engagement
Build the reporting leadership reads: retention, engagement, risk, expansion
Own the product feedback loop. You see what's breaking first, and engineering needs it from you