Your responsibilities will include:

Architecting and owning the ML foundations for commerce discovery: user, item, and interaction embeddings that power personalized recommendations across shop surfaces (homepage, cart, post-purchase, wishlist, and more).

Designing and deploying scalable real-time recommendation and ranking systems that support a growing catalog of 1P and 3P items across heterogeneous game publisher inventories.

Building ML-powered marketing targeting systems that identify the right users for the right campaigns , new buyer discounts, drop campaigns, weekly deals, and seasonal promotions , driving conversion without conditioning users to wait for discounts.

Leveraging Discord's unique social graph to build social commerce ML: gifting recipient prediction, group buying conversion modeling, and friend-group recommendations that differentiate Discord from traditional game storefronts.

Driving deep learning A/B testing infrastructure and model monitoring to translate experimentation results into actionable product decisions.

Partnering closely with Shop, Game Commerce, Revenue Infra, ML Infra, and Data Engineering teams to define ML requirements, surface integration points, and influence the commerce roadmap.

To be successful in this role, you will need:

4+ years of experience as a Machine Learning Engineer, with a track record of owning and shipping recommendation or personalization systems end-to-end.

Deep expertise in applied deep learning , particularly embedding models, two-tower architectures, and retrieval/ranking systems for e-commerce or content recommendation.

Strong proficiency in Python and deep learning frameworks (PyTorch preferred).

Experience building and operating real-time ML serving infrastructure at scale, including feature stores, model serving, and A/B testing frameworks.

Demonstrated ability to work in early-stage, high-ambiguity environments and build ML systems from the ground up, not just improve existing ones.

Experience translating ML evaluation metrics and experiment results into product roadmap decisions and business impact.

Strong cross-functional instincts , you're comfortable partnering with product, engineering, data science, and business stakeholders to align on priorities and drive execution.

Bonus skills include experience applying graph ML or social network signals (social affinities, community behavior) to recommendation or personalization problems, familiarity with personalized marketing systems: lifecycle targeting, audience segmentation, and campaign optimization, and familiarity with loyalty, rewards, or incentive programs.

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