<p><em>The AI is proven. Now build the product that turns capability into a category.</em></p> <p><em><span data-contrast="none">On-site | Vancouver Office – 675 W Hastings St.</span></em><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></p> <p><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></p> <p><strong>The Problem We're Solving</strong></p> <p>EviSmart has built AI that automates the core of dental lab work. The unsolved problem is product: taking that capability and shaping it, from the lab's real needs outward, into something labs adopt, rely on, and build their operations around.</p> <p> </p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 3">Why EviSmart</span></span></strong></p> <ul> <li style="line-height: 2;">300 people. Two hubs: Vancouver HQ and Manila operations.</li> <li style="line-height: 2;">145% year-over-year SaaS growth — the market is responding.</li> <li style="line-height: 2;">28 countries. One platform. The dental industry's Autopilot.</li> <li style="line-height: 2;">An in-house AI model research and development team building proprietary intelligence.</li> </ul> <p> </p> <p><strong>A Note from the Team </strong></p> <p><em>"We're not looking for someone who writes tickets based on what engineers tell them is possible. We need someone who spends time with labs and dentists, understands the workflow deeply enough to make real product tradeoffs, and can earn credibility with an ML team by knowing what they're actually building. If you've been the person who brings customer clarity and commercial rigor to an AI product — and made both the research team and the business better for it — that's the conversation we want to have."</em></p> <p><strong>— Paolo Kalaw, CEO, EviSmart</strong></p> <p> </p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 3">What </span><span data-ccp-parastyle="heading 3">You’ll</span><span data-ccp-parastyle="heading 3"> Own</span></span></strong><span data-ccp-props="{"134233117":false,"134233118":false,"134245418":true,"134245529":true,"335551550":0,"335551620":0,"335559738":281,"335559739":281}"> </span></p> <ul> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Own</strong> the strategy and roadmap across the breadth of EviSmart's AI portfolio.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Define</strong> the product itself: the problem it solves, the core workflows it owns, and the moments the AI has to earn a user's trust.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Instrument</strong> Every feature before it ships — adoption funnels, cohort analysis, usage patterns. Product decisions here are backed by evidence, not intuition or loudest-customer-wins.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Own</strong> Competitive positioning against primary market challengers — know where EviSmart's differentiation is real, where it's at parity, and where it needs to close ground.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Drive</strong> User validation continuously: conduct interviews, observe workflows, and build a user insight library the entire squad references for every product decision.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Coordinate</strong> With the design operations team on the product side of the transition as AI handles more commodity casework and the team shifts toward complex cases.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Expand</strong> Over time into product leadership for the dashboard platform — PLG hooks, self-serve signup, conversion funnels, and in-product upsell triggers.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;"><strong>Nurture</strong> The existing platform PM — develop their product rigor, user validation practice, and roadmap prioritization while sharing accountability for platform outcomes.</li> </ul> <p> </p> <p><strong>What You'll Get</strong></p> <ul> <li class="whitespace-normal break-words pl-2" style="line-height: 2;">A seat where AI model decisions and product decisions meet.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;">Direct customer access across 2,000+ labs — the feedback loop is fast, the stakes are real, and user research here isn't a quarterly exercise.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;">A category-defining problem in a $40B industry where the incumbent software hasn't been rebuilt in a generation.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;">Production AI tooling from day one: Claude, Cursor, and LLM-powered workflows built in-house — not a sandbox, not a pilot.</li> <li class="whitespace-normal break-words pl-2" style="line-height: 2;">Competitive compensation with salary range disclosed at offer.</li> </ul> <p> </p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 3">How We Work</span></span></strong></p> <p>We ship before we’re 100% certain. We write things down because we have two offices and memory is lossy. We debate loudly and move without resentment. We treat the customer’s real problem as more important than an elegant internal process. If you’ve spent time waiting for permission to try something obvious — you’ll notice the difference here immediately. <span data-ccp-props="{"335551550":0,"335551620":0}"> </span></p> <p> </p> <p><strong>The Question You're Probably Asking</strong></p> <p><em>"Dental sounds niche. Is this really a space where a strong AI product background translates? The strongest AI PMs wo