Full-stack Growth Engineer
Slate
| Company | Slate |
| Category | Engineering |
| Location | Remote (UTC-5) to UTC+2 |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | USD 95k–200k |
| Posted | 27 May 2026 |
| Last verified | 31 Jul 2026 |
| Source | Employer career page (ashby) |
Description
FULL-STACK GROWTH ENGINEER
THE PITCH
Slate is the creative workspace that social teams at Amazon, Hoka, the NFL, and the Premier League use every day to ship on-brand content fast. We have just closed three consecutive record-breaking new business months — our biggest new business quarter ever — and we are now building the next chapter of the company: product-led growth.
Today, Slate is a tool for specialists. Marketing drives more than a hundred trials and demos a month into the funnel, but there is no self-serve layer to convert them, and the product is not yet approachable enough for users who arrive on their own. The opportunity in front of us is bigger than improving the trial — it is to open up Slate to a much broader population of users, support a long tail of self-serve customers at scale, and build a real second growth engine alongside the enterprise motion.
We are hiring our Full-stack Growth Engineer, the first person at Slate dedicated to PLG, to make that happen. You will build and own the self-serve surface end-to-end: sign-up, the first-run experience, the path to paid, and the experimentation discipline that determines what we build next.
This is a role that pulls on expertise from multiple disciplines. You will build like a senior full-stack engineer, prioritize like a PM, instrument like a data engineer, and reason like an experimentalist. You will be the in-house expert the rest of engineering learns growth from. And because PLG touches product, design, marketing, and sales, you will be cross-functional from day one.
You are not joining the rocket ship after it has launched. You are helping us build it.
WHAT YOU'LL ACTUALLY DO
Build the self-serve surface, end-to-end
- Design and ship a self-serve sign-up and payment flow — from anonymous visitor to paid user, without a sales touch
- Define and instrument trial limits — AI credit budgets, storage caps, seat counts — generous enough to wow, tight enough to upgrade
- Surface high-intent upsell signals to the sales team so PLG and enterprise compound rather than compete
- [Nice to have] Integrate billing (Stripe / recurring payments) and the operational realities that come with it: trials, upgrades, downgrades, dunning, tax, refunds
Fix the "cold start" problem: make Slate approachable from minute one
- Own the new-user journey: get someone from sign-up to their first piece of branded content fast, before they bounce
- Apply the hook model: Trigger → Action → Variable Reward → Investment — to every step of the activation surface
- Help shape the product itself, not just the wrapper around it. Slate needs to be usable by non-specialists, and you will influence what changes to get there
- Work with our AI team on building innovative technology to enable new users to see their own brand inside the product within minutes
Bring a real experimentation discipline to Slate
- Stand up the experimentation platform: events, attribution, cohort tracking, exposure logging, the full pipeline
- Run experiments with proper controls and test groups, statistical significance, and pre-registered guardrails / anti-goals — not vibes
- Measure both short-term conversion AND longer-term business outcomes (retention, paid plan uptake, expansion, LTV) using longitudinal measurement
- Treat product changes as hypotheses: design them, run them, ship or kill based on what the data says
- Operate as our in-house growth engineering expert — raise the experimentation bar across the engineering team, not just within your own surface
YOU'D BE A GREAT FIT IF
You have the experience:
- 5+ years building production systems with a growth and experimentation specialism (not general product engineering with growth on the side)
- Comfortable in TypeScript / Node.js and a modern frontend framework
- Deep familiarity with experimentation: controls, test groups, statistical significance, guardrails, anti-goals, exposure logging
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