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Salary: USD 272,000 - 340,000 / annual
Discord is seeking a Staff Machine Learning Engineer to join the Consumer Revenue ML team. This team builds machine learning systems across Discord's core revenue surfaces—Shop, Nitro, Server Subscriptions, and Gifting—developing ranking, targeting, and recommendation systems that connect users to the right products, subscriptions, and content.
In this role, you will lead organization-wide ML initiatives and drive technical strategy for consumer revenue systems. You'll work on applied machine learning problems at scale, including building recommendation systems, personalization engines, and targeting platforms. The position offers the opportunity to take ML systems from concept through production deployment, working with deep learning models and large-scale data infrastructure.
You'll collaborate across multiple product verticals and engineering teams, translating experimental results into product roadmap decisions. The role emphasizes both technical depth in machine learning and the ability to communicate complex technical concepts to non-technical stakeholders.
The position is US-based and can be fully remote or hybrid from the San Francisco office.
**Requirements:**
- 8+ years of experience in applied Machine Learning
- Ph.D. or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or related field
- Strong expertise in applied deep learning and mainstream RecSys model architectures (two-tower, transformer-based models, multi-task learning)
- Strong proficiency in Python and ML frameworks (PyTorch, JAX, or TensorFlow)
- Track record of building ML systems from 0→1 in ambiguous, early-stage environments and scaling them to production
- Strong product and business intuition with ability to translate experiment results into roadmap decisions
- Excellent communication and collaboration skills; ability to lead cross-functional technical initiatives
- Ability to thrive in ambiguous, technically challenging environments
**Preferred Qualifications:**
- Experience building internal ML platforms/tooling (shared data standards, targeting endpoints, recommender libraries) adopted by multiple product teams
- Familiarity with personalized marketing systems (lifecycle targeting, audience segmentation, lookalikes, campaign optimization)
- Deep expertise in distributed training (PyTorch on GPU, Ray, Anyscale) and large-scale data processing pipelines (Chronon, Spark, Flink)
