Staff Software Engineer, Machine Learning (Consumer Revenue)

About the role

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: play video games. 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.

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!

What You'll Be Doing

• 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.
• 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, with the ability to translate experiment results into roadmap decisions.
• Excellent communication and collaboration skills - able to lead cross-functional technical initiatives across multiple verticals and keep stakeholders educated and aligned.
• The ability to thrive in ambiguous environments, energized by open-ended, technically challenging problems.

What you should have

• 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).

Additional information

This position is US-based and can be remote but if you live in the Bay Area, you're welcome to work from our beautiful SF office.

Pay and benefits

The US base salary range for this full-time position is $272,000 to $340,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.