Senior Software Engineer, Machine Learning (Commerce)

Added
1 minute ago
Type
Full time
Salary
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Related skills

python pytorch recommender systems embeddings feature stores

πŸ“‹ Description

  • 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 campaigns β€” new buyer discounts, drop campaigns, weekly deals, and seasonal promotions β€” driving conversion without conditioning users to wait for discounts.
  • Leverage Discord's 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 β€” 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.
  • Strong cross-functional collaboration with product, engineering, data science, and business stakeholders.
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