Senior Machine Learning Engineer (Growth)

Location: San Francisco Bay Area, US

Base salary: $220,000–$302,500 USD per year, excluding equity and benefits.

Company

Discord is a gaming-focused multiplatform community platform helping people connect around games and shared interests.

What you will do

• Design, build, and scale recommendation systems connecting new users with communities and content, including candidate generation, ranking, and online serving.
• Solve cold-start personalization using limited signals to recommend servers, people, and content.
• Build and deploy machine learning models that improve new-user retention, using user and server embeddings.
• Take ML solutions from initial concept to production at scale, including experimentation and scalable system design.
• Collaborate with the cross-functional Onboarding squad on roadmap decisions, technical trade-offs, priorities, and progress.

Requirements

• 5+ years of experience in Machine Learning, Data Science, or Applied Science, including a record of shipping recommendation or personalization systems end to end.
• Hands-on experience with modern recommendation systems, including retrieval and ranking pipelines, embedding-based retrieval such as two-tower models, and deep learning models such as transformers or 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 interest in building 0→1 ML applications based on user feedback and real-world impact.
• Excellent communication and collaboration skills, with the ability to represent ML on a cross-functional squad and align stakeholders on trade-offs, priorities, and progress.
• Ability to thrive in ambiguous environments and tackle open-ended, technically challenging problems.

Nice to have

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

Culture & Benefits

• Medical, dental, and vision insurance.
• 401(k) plan, life and disability insurance, paid time off, and paid parental leave.
• Equity eligibility in addition to base salary.
• Reasonable accommodations are available during the interview process.

Hiring process

• Applications are accepted on an ongoing basis.
• Applicants may request reasonable accommodations during the interview process.