We're looking for a Senior Machine Learning Engineer to join our Engagement ML team, working as part of the cross-functional Onboarding squad in Discord's Growth organization. You'll design and build 0→1 end-to-end ML solutions that help brand-new users find their place on Discord: the servers, people, and content that turn a first session into a long-term connection to the product. If this excites you, keep reading!

What You'll Be Doing

• Design, build, and scale the recommendation systems that connect new users to communities and content, from candidate generation and ranking through to online serving.
• Solve cold-start personalization for users with little or no history on Discord, turning the limited signals available into recommendations for servers, communities, and content.
• Build and deploy ML models that lift new-user retention, combining purpose-built approaches with shared foundations such as user and server embeddings.
• Collaborate cross-functionally to shape the Onboarding roadmap, balancing fast learning experiments with the long-term complexity and scalability of the systems behind them

What you should have

• 5+ years of experience in Machine Learning, Data Science, or Applied Science, with a track record of shipping recommendation or personalization systems end to end.
• Hands-on experience building modern recommendation systems: retrieval and ranking pipelines, embedding-based retrieval (e.g. two-tower models), and deep learning models (e.g. transformers, multi-task learning).
• Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
• Experience taking ML systems from ideation to production at scale, including feature pipelines, model serving, monitoring, and online experimentation.
• Strong product intuition and a passion for building 0→1 ML applications grounded in user feedback and real-world impact.
• Excellent communication and collaboration skills, able to serve as the ML voice on a cross-functional squad and keep stakeholders aligned on trade-offs, priorities, and progress.
• The ability to thrive in ambiguous environments, energized by tackling open-ended, technically challenging problems

Bonus Points If You Have:

• Experience with cold-start or new-user personalization, onboarding flows, or preference elicitation.
• Built user representation models or applied social-graph signals to recommendation problems.
• Worked on growth or engagement problems for consumer products, such as community and content recommendations, user activation, or retention.
• Experience with large-scale recommendation systems, data infrastructure, distributed computing, and vector search.