AI Enablement Lead

Meet Upside

We created Upside to transform brick-and-mortar commerce. Our technology uses the sophistication of online retail—profit measurement, attribution, and incrementality—to provide users with more value on their everyday purchases and brick-and-mortar businesses with new, profitable customers. We've helped millions of users earn 2 to 3 times more cashback than any other product, and hundreds of thousands of brick-and-mortar businesses earn measurable profit. Billions of dollars in commerce run through the Upside platform every year, and that value goes directly back to our retailer partners, the consumers they serve, and important sustainability initiatives.

The Opportunity

Individual AI gains don't compound on their own. Shared infrastructure does.

Every company right now is watching employees get faster with AI, one person and one workaround at a time. That's real, but it doesn't add up to anything durable: the gains stay locked in whoever figured out the trick, and the next person starts from zero. The organizations that pull ahead won't be the ones with the most individual AI users. They'll be the ones that turned individual AI use into shared infrastructure: connected systems, encoded company knowledge, a marketplace of reusable skills (packaged AI capabilities people can install and reuse instead of rebuilding from scratch), and workflows that get smarter the more people use them.

Upside is building that infrastructure. It's called Ascent: a shared layer of connections, encoded company knowledge, and tooling that sits on top of Claude Enterprise, so that getting value from AI doesn't require every employee to build their own one-off setup. Ascent already has a track record. It's the platform behind Upside's engineering org shipping 8-week projects in 3, doubling our historical PR throughput and institutionalizing spec-driven-development org-wide. The same infrastructure is now being extended so the rest of the company, not just engineers, can get that kind of leverage.

The AI Enablement Lead owns Ascent's roadmap end to end, spanning both the engineering-facing half (Software Factory, a program of tools and workflows purpose-built for how engineers ship code) and the company-wide half (the Enterprise Track, the same kind of AI leverage rebuilt for everyone else at Upside, regardless of technical background). The job is translating a fast-moving, technical, still-being-invented platform into sequenced priorities, clear specs, and decisions the business can act on. This role reports directly to the VP of R&D Intelligence, Systems and Enablement (RISE).

Why This Role Exists Now

Upside's AI enablement work is making a transition that most companies haven't started yet: from point solutions to platform investment.

Point-solution AI adoption looks like progress: more people using Claude, more one-off skills, more individual productivity wins. But it plateaus. It doesn't compound, it's not repeatable across teams, and nobody can tell you what it's actually worth. Platform investment is different. It requires someone to hold the whole roadmap in their head, make sequencing calls across programs with different maturity levels, and be accountable for whether the infrastructure is actually working, not just whether people like it.

That's a materially different job than the ad hoc coordination that got Ascent this far. It needs a real owner: someone who can sit across two programs at different stages of maturity, make prioritization calls that hold up under scrutiny, and keep the roadmap coherent as the platform (and the underlying AI tooling itself) keeps changing under it.

Key Initiatives

These are the bets this role will drive. Ascent is mid-build on both programs it contains; treat these as the kind of work this role owns, not a fixed deliverables list. The specific roadmap is this role's to define.

1. Own the Full-Ascent Roadmap

Ascent is one platform with two programs on shared infrastructure: Software Factory (the engineering program) and the Enterprise Track (a parallel program that brings the same kind of AI leverage to everyone else at Upside, regardless of technical background). They're at different points of maturity, sequenced deliberately with engineering first because it was the highest-leverage bet. They share underlying infrastructure, though, and now both get real investment at once.

You'll own prioritization and sequencing across both, not just the less-mature Enterprise Track. That means making real trade-off calls: where shared infrastructure work pays off both programs at once, where the two programs' needs diverge and each needs its own path, and how to sequence Enterprise Track investment without stalling Software Factory's continued build-out.

2. Close the Enterprise Track's Maturity Gap

Software Factory and the Enterprise Track are evaluated against the same six capabilities: whether the systems people need are actually connected with no manual setup (connected), whether Upside-specific know-how is built into the tools rather than generic AI advice (Upside-native), whether people can discover, install, and publish reusable AI skills without friction (marketplace), whether the tools improve from how people actually use them (gets smarter), whether repeatable workflows can be automated and scheduled (automate), and whether it's safe and easy to build new software with AI (build). On every one of these, the Enterprise Track is materially behind Software Factory. Some of the gap is infrastructure (key systems like Salesforce aren't connected yet), some is depth of encoding (Upside-specific know-how hasn't been built into non-engineering skills the way it has for engineering), and some is access (there's no non-technical path yet to contribute or update a skill).

You'll drive the roadmap that closes this gap: deciding which capability to invest in first, what "good enough" looks like at each stage, and how to prove out value on the Enterprise Track the way Software Factory already has on engineering.

3. Build the Feedback Loop That Ties Ascent to Business Value

Right now, Ascent's "gets smarter" capability is one of its weakest across both programs: usage and session data are already being captured, but turning that into insight requires manual, technical analysis, and there's no mechanism that connects AI spend or skill usage to the business value it's actually producing.

You'll own building that feedback loop: instrumentation, reporting, and a working model of what "this skill or agent is worth it" means, so that Ascent's roadmap is driven by evidence of what's working, not by whoever asks the loudest.

Ongoing Responsibilities

• Partner with the teams building Ascent as a product owner partners with engineering: you own the roadmap, the specs, and the prioritization calls; they own technical execution and design. Software Factory and the Enterprise Track each have their own dedicated engineering teams and their own managers. This role has no direct reports, and your influence comes from the quality of your specs and the credibility of your calls, not from authority over anyone's headcount.

• Own and maintain the Ascent roadmap across both Software Factory and the Enterprise Track, and be the person accountable for its sequencing and trade-offs

• Write specs and requirements clear enough that engineering teams you don't manage can execute against them without you in the room

• Track and report on Ascent's maturity across its core capabilities, and use that to justify what gets invested in next

• Represent Ascent to stakeholders outside RISE, explaining what it does, what it doesn't yet do, and what's coming, in language that lands with non-technical audiences

• Stay current, hands-on, with the AI coding/agent tooling landscape so the roadmap reflects real friction and real capability, not secondhand reports of it.

What We're Looking For

Must have:

• 5-8 years leading platform-level or 0-to-1 initiatives from ambiguity to a working roadmap, not just running isolated features or one-off projects

• Deep, hands-on personal experience building with AI coding agents and AI dev tooling: enough to have felt where off-the-shelf tools break down and turned that friction into product judgment. This isn't optional context; it's the thing that makes the roadmap calls credible.

• Comfort building working, code-based prototypes yourself using AI coding agents. You're not expected to ship production code, but if you can't sit down and get an agent to build you a working prototype, you won't be able to tell a real capability gap from a skill-issue complaint.

• Demonstrated ability to write specs and drive prioritization across a technical program without direct authority over the people building it