Product ML Engineer

<h2><strong>About us</strong></h2><p style="min-height:1.5em">Founded in 2019 and part of Y Combinator's 2020 cohort, <a target="_blank" rel="noopener noreferrer" class="c-link c-link--focus-visible c-link--underline" href="https://www.photoroom.com"><u>Photoroom</u></a> is the leading visual solution for e-commerce.<br /><br />We've raised Series B funding and reached 300+ million users worldwide, processing over 5 billion images annually and serving both small businesses and major enterprises like Amazon, DoorDash, and Decathlon through our mobile app, web platform, and API.<br /><br />We're a team of 100+ passionate builders giving e-commerce businesses superpowers to create visuals that help them grow, making the hardest parts of selling online disappear. We focus on craft, innovation, and collaboration to create exceptional impact for e-commerce businesses worldwide.<br /><br /></p><h2>Role Summary</h2><p style="min-height:1.5em">We're looking for a <strong>Product ML Engineer</strong> to join one of our product experience teams — SMB, Scaler or Enterprise — and own the ML side of short, high-impact projects.</p><p style="min-height:1.5em">You'll work on focused ML problems with a clear product outcome, sometimes fine-tuning one of our models, sometimes integrating the best externally available model, but always choosing the fastest and most effective path to customer value.</p><p style="min-height:1.5em">You'll bring the technical depth of an Applied Scientist while operating with the pace and pragmatism of a product engineer. You'll own the journey from framing the problem through to shipping, measuring and iterating on the feature in production.</p><p style="min-height:1.5em"><strong>This is not a research role.</strong> Your time horizon is weeks rather than quarters, and success is measured by what reaches users and the impact it creates.<br /></p><h2>About the role</h2><ul style="min-height:1.5em"><li><p style="min-height:1.5em"><strong>Embed directly within a product team.</strong> Work closely with the SMB, Scaler or Enterprise team on focused ML problems tied to clear customer and business outcomes.</p></li><li><p style="min-height:1.5em"><strong>Own ML projects end to end.</strong> Frame the problem, choose the approach, execute the plan, ship the feature and monitor its performance in production.</p></li><li><p style="min-height:1.5em"><strong>Make the build vs. buy decision.</strong> Determine when to fine-tune or build a model yourself versus integrating an external model, API or existing library.</p></li><li><p style="min-height:1.5em"><strong>Move from idea to production quickly.</strong> Work on projects scoped in weeks rather than quarters, prioritising rapid iteration and customer impact.</p></li><li><p style="min-height:1.5em"><strong>Work closely with the ML team.</strong> Leverage shared infrastructure, evaluation tooling and expertise while remaining embedded in the product team's priorities and cadence.</p></li><li><p style="min-height:1.5em"><strong>Stay close to users.</strong> Get rapid feedback from customers, monitor how people use what you've shipped and use those insights to drive the next iteration.</p></li><li><p style="min-height:1.5em"><strong>Balance technical depth with pragmatism.</strong> Know when deeper modelling work is necessary and when an existing solution is already good enou