We are looking for a Senior Machine Learning Engineer to join our Revenue ML team at Discord. This role sits at the intersection of Discord's two most strategic revenue pillars , our growing 1P Shop and our newly launched Game Commerce platform. You'll be the founding ML voice for commerce discovery and personalization, building systems from the ground up that power recommendations, social commerce mechanics, and marketing targeting across both first-party and third-party storefronts.
\nYour responsibilities will include:
\nArchitecting 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).
\nDesigning and deploying scalable real-time recommendation and ranking systems that support a growing catalog of 1P and 3P items across heterogeneous game publisher inventories.
\nBuilding 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.
\nLeveraging 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.
\nDriving deep learning A/B testing infrastructure and model monitoring to translate experimentation results into actionable product decisions.
\nPartnering 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.
\nTo be successful in this role, you will need:
\n4+ years of experience as a Machine Learning Engineer, with a track record of owning and shipping recommendation or personalization systems end-to-end.
\nDeep expertise in applied deep learning , particularly embedding models, two-tower architectures, and retrieval/ranking systems for e-commerce or content recommendation.
\nStrong proficiency in Python and deep learning frameworks (PyTorch preferred).
\nExperience building and operating real-time ML serving infrastructure at scale, including feature stores, model serving, and A/B testing frameworks.
\nDemonstrated ability to work in early-stage, high-ambiguity environments and build ML systems from the ground up, not just improve existing ones.
\nExperience translating ML evaluation metrics and experiment results into product roadmap decisions and business impact.
\nStrong cross-functional instincts , you're comfortable partnering with product, engineering, data science, and business stakeholders to align on priorities and drive execution.
\nBonus 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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