Staff Software Engineer, Machine Learning

Summary

Join Discord's Discovery/Engagement ML team as a Staff Software Engineer and help shape the future of gaming. You will design, build, and scale robust recommender systems used by tens of millions of users daily. Leverage deep learning, reinforcement learning, and optimization to enhance user experience and drive engagement. Collaborate cross-functionally to define ML-driven product roadmaps. This role requires significant experience in applied machine learning and building performant systems at scale. The position is US-based and can be remote, with a salary range of $272,000 to $306,000 + equity + benefits.

Requirements

• 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
• Deep expertise in mainstream RecSys model architecture (e.g. two-tower, transformer-based model, multi-task learning, wide and deep etc.)
• Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow
• Extensive experience building performant machine learning systems at scale and have driven execution from ideation to production implementation
• Strong product intuition and a passion for building ML applications grounded in user feedback and real-world impact
• Excellent communication and collaboration skills—able to lead complex, cross-functional technical initiatives and keep stakeholders aligned through clear updates and problem-solving
• The ability to thrive in ambiguous environments, and are energized by tackling open-ended, technically challenging problems

Responsibilities

• Raise the technical bar for the team by setting architectural direction, tackling systemic challenges, and identifying opportunities for innovation and efficiency
• Design, build and scale robust, high-throughput, low latency recommender systems that power product features used by tens of millions of users every day
• Build and deploy advanced ML models, leveraging deep learning, reinforcement learning, and optimization to drive engagement and enhance user experience
• Collaborate cross-functionally to shape ML-driven product roadmaps, balancing speed of iteration with long-term system complexity and scalability

Preferred Qualifications

• Experience with whole-page optimization in marketplace or multi-objective ranking environments
• Worked on notification systems or applied reinforcement learning in production settings
• Built models for user representation learning or user modeling to power personalization
• Deep expertise in distributed training (e.g. PyTorch DDP, Ray Train) and large-scale data processing pipelines (e.g., Spark, Flink)

Benefits

• Equity
• Benefits