Staff Software Engineer, Machine Learning (Consumer Revenue)

Discord•San Francisco, CA

3d•$272,000 - $340,000•Remote

About The Position

Discord is seeking a Staff Machine Learning Engineer for its Consumer Revenue ML team. This team focuses on applying Machine Learning to Discord's core revenue streams, including Shop, Nitro, Server Subscriptions, and Gifting. The role involves building ranking, targeting, and recommendation systems to connect users with relevant products, subscriptions, and content. The ideal candidate will have a history of leading organization-wide initiatives.

Requirements

8+ years of experience in applied Machine Learning.
Ph.D. or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Strong expertise in applied deep learning and mainstream RecSys model architecture (e.g. two-tower, transformer-based models, multi-task learning).
Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
A track record of building ML systems from 0→1 in ambiguous, early-stage environments, and taking them to production at scale.
Strong product and business intuition.
Excellent communication and collaboration skills.
The ability to thrive in ambiguous environments, energized by open-ended, technically challenging problems.

Nice To Haves

Built internal ML platform/tooling (shared data standards, targeting endpoints, recommender libraries) adopted by multiple product teams.
Familiarity with personalized marketing systems — lifecycle targeting, audience segmentation and lookalikes, campaign optimization.
Deep expertise in distributed training (e.g. PyTorch on GPU, Ray, Anyscale) and large-scale data processing pipelines (e.g. Chronon, Spark, Flink).

Responsibilities

Build ranking, targeting, and recommendation systems for Discord's core revenue surfaces.
Apply Machine Learning to connect users to the right products, subscriptions, and content.
Lead org-wide initiatives in Machine Learning.
Translate experiment results into roadmap decisions.
Lead cross-functional technical initiatives across multiple verticals.
Keep stakeholders educated and aligned on technical initiatives.