Product ML Engineer

Founded in 2019 and part of Y Combinator's 2020 cohort, Photoroom is the leading visual solution for e-commerce. 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. We're a remote-friendly 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, creating exceptional impact for e-commerce businesses worldwide.

Summary We're looking for a Product ML Engineer to join one of our product experience teams — SMB, Scaler or Enterprise — and own the ML side of short, high-impact projects. 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. 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. This is not a research role. Your time horizon is weeks rather than quarters, and success is measured by what reaches users and the impact it creates.

Embed directly within a product team. Work closely with the SMB, Scaler or Enterprise team on focused ML problems tied to clear customer and business outcomes. Own ML projects end to end. Frame the problem, choose the approach, execute the plan, ship the feature and monitor its performance in production. Make the build vs. buy decision. Determine when to fine-tune or build a model yourself versus integrating an external model, API or existing library. Move from idea to production quickly. Work on projects scoped in weeks rather than quarters, prioritising rapid iteration and customer impact. Work closely with the ML team. Leverage shared infrastructure, evaluation tooling and expertise while remaining embedded in the product team's priorities and cadence. Stay close to users. Get rapid feedback from customers, monitor how people use what you've shipped and use those insights to drive the next iteration. Balance technical depth with pragmatism. Know when deeper modelling work is necessary and when an existing solution is already good enough to ship. Write production code. Work directly in the product team's codebase rather than operating primarily through research notebooks and experiments. Own quality in production. Build sound evaluations, monitor performance and investigate failures once the feature is live.

You've shipped ML into production. You have 2+ years of hands-on experience building and shipping ML-powered products, with strong experience in PyTorch or equivalent frameworks such as TensorFlow or JAX. You have applied-science depth. You can fine-tune vision or diffusion models, design meaningful evaluations and debug training runs when necessary. You're a pragmatic builder. You care about solving the customer's problem rather than proving that your own model is the best approach. You'll happily use an external model, API or existing library when that's the fastest route to value. You're product-minded. You measure success through shipped features, user behaviour and business outcomes rather than papers or benchmarks alone. You're comfortable in production codebases. You can move between experimentation and production engineering, writing maintainable code that lives inside a real product. You have strong ownership. You're comfortable taking an ambiguous ML problem, deciding what matters, choosing an approach and driving it through to production. You iterate quickly. You enjoy short feedback loops and would rather