AI Growth Engineer (Revenue Prediction & Campaign Infrastructure)

You own the systems that turn raw signal into pipeline. Prediction models, campaign infrastructure, the AI that generates personalized outreach at scale. You inherit live systems carrying real revenue, and what you build is how we sell.

What you'll own

• Predict the account before anyone touches it. Models for estimator count, seats, and expected ARR, each with confidence bands so reps know when to trust the number. Conversion scoring on top, and you own it through retraining as the data and the market shift.

• Run the campaigns that carry real pipeline. Multiple segments, multiple channels: email, LinkedIn, voicemail. Every campaign is code you own. Routing, retries, dedup, deliverability. You're on the hook for uptime and output quality, not just the initial build.

• Generate the personalization. Per-rep landing pages and AI avatar video, tracked end to end so a booked demo attributes to a specific asset.

• Ground the AI in real calls. Call intelligence as a knowledge endpoint feeding prep docs, follow-up emails, avatar scripts, and page copy, so outbound cites objections we've genuinely heard, not generic messaging.

• Turn signals into tasks. Job changes, promotions, news, clicks. Scored and routed into prioritized rep actions, not another ignored notification.

What you'll bring

• 5+ years in growth engineering, RevOps, or a technical marketing or sales ops role you can tie directly to pipeline or revenue moved.

• Strong fluency in a programming language. You write, debug, and validate production code, not just generate it.

• Applied ML experience. You've built, trained, and maintained a model in production, and you can tell a non-technical stakeholder exactly how much to trust it.

• Deep comfort with REST APIs, webhooks, OAuth, and rate limits, plus real patience for the debugging that follows when vendor APIs fail in undocumented ways.

• Strong SQL and enough data sense to know when a model is telling you something real, and to say so out loud when it isn't.

• Hands-on with a modern GTM stack: HubSpot or Salesforce, plus tools like Clay, Apollo, Instantly, n8n.

• Practical LLM experience. Prompting, function calling, evaluating output, knowing when a model is the wrong tool.

• A working v1 this week beats a perfect spec next month, and the judgment to know which one a given problem actually needs.

Bonus

Construction or AEC background. ML research background. Frontend or backend depth. Experience scaling a GTM engineering function as the team around it grows.

More about the role

You're inheriting a function that already exists and already carries real pipeline. There's no greenfield here. The job is judgment under live conditions: knowing what to fix first, what to leave alone, and when a quick v1 beats a perfect spec.

The stack is wide, and you'll move between retraining a model and fixing a broken webhook in the same week. Vendor APIs fail in undocumented ways. Labels are imperfect and attribution is never fully clean, and you have to be comfortable deciding on noisy data while saying out loud how noisy it is.

We are an equal opportunity employer committed to building a diverse team. We welcome applications from candidates of all backgrounds who are passionate about using technology to transform the construction industry.