Staff Software Engineer, Machine Learning (Consumer Revenue)

<div class="content-intro"><p>Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: <strong>play video games.</strong> Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games.</p></div><p><strong><span class="discussion-id-3e2f46fd-48aa-8020-9d78-001c2dd6e15b notion-enable-hover" data-token-index="0">We're looking to hire a Staff Machine Learning Engineer on our Consumer Revenue ML team. This team applies Machine Learning across Discord's core revenue surfaces — Shop, Nitro, Server Subscriptions, and Gifting — building the ranking, targeting, and recommendation systems that connect users to the right products, subscriptions, and content. If this excites you and you’ve led org-wide initiatives in the past, keep reading!</span></strong></p> <p><strong>What You'll Be Doing</strong></p> <ul> <li>8+ years of experience in applied Machine Learning, inclusive Ph.D. or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.</li> <li>Strong expertise in applied deep learning and mainstream RecSys model architecture (e.g. two-tower, transformer-based models, multi-task learning).</li> <li>Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.</li> <li>A track record of building ML systems from 0→1 in ambiguous, early-stage environments, and taking them to production at scale.</li> <li>Strong product and business intuition, with the ability to translate experiment results into roadmap decisions.</li> <li>Excellent communication and collaboration skills — able to lead cross-functional technical initiatives across multiple verticals and keep stakeholders educated and aligned.</li> <li>The ability to thrive in ambiguous environments, energized by open-ended, technically challenging problems.</li> </ul> <p><strong>What you should have</strong></p> <ul> <li>Built internal ML platform/tooling (shared data standards, targeting endpoints, recommender libraries) adopted by multiple product teams.</li> <li>Familiarity with personalized marketing systems — lifecycle targeting, audience segmentation and lookalikes, campaign optimization.</li> <li>Deep expertise in distributed training (e.g. PyTorch on GPU, Ray, Anyscale) and large-scale data processing pipelines (e.g. Chronon, Spark, Flink).</li> </ul> <p><em>&nbsp;</em></p> <p><em><strong>This position is US-based and can be remote but if you live in the Bay Ar