Senior Software Engineer, Machine Learning (Commerce)

Discord is a platform used by over 200 million people for gaming and social interactions. They are looking for a Senior Machine Learning Engineer to join their Revenue ML team, focusing on building systems that power recommendations and marketing targeting for their commerce platform.

Responsibilities:

• Architect and own 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)
• Design and deploy scalable real-time recommendation and ranking systems that support a growing catalog of 1P and 3P items across heterogeneous game publisher inventories
• Build 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
• Leverage 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
• Drive deep learning A/B testing infrastructure and model monitoring to translate experimentation results into actionable product decisions
• Partner 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

Requirements:

• 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
• 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
• Familiarity with loyalty, rewards, or incentive programs