Staff Software Engineer, Machine Learning (Consumer Revenue)

Role overview

This Staff Machine Learning Engineer role applies machine learning across a platform's core consumer revenue surfaces, including shop, premium subscriptions, server subscriptions, and gifting products. The position builds the ranking, targeting, and recommendation systems that connect users to the right products and content, leading org-wide initiatives and partnering across multiple product verticals. The work spans 0→1 design through large-scale production systems, with strong emphasis on translating experiment results into roadmap decisions.

Responsibilities

- Design and build ranking, targeting, and recommendation systems across consumer revenue products using mainstream recommender architectures. - Lead cross-functional ML initiatives across multiple product verticals, keeping stakeholders educated and aligned throughout delivery. - Take ML systems from 0→1 in ambiguous, early-stage environments and scale them to production. - Build internal ML platform and tooling such as shared data standards, targeting endpoints, and recommender libraries adopted by multiple product teams. - Apply expertise in distributed training (for example PyTorch on GPU, Ray, Anyscale) and large-scale data processing pipelines such as Chronon, Spark, or Flink. - Contribute to personalized marketing capabilities including lifecycle targeting, audience segmentation and lookalikes, and campaign optimization.

Requirements

- 8+ years of experience in applied machine learning, inclusive of a 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 recommender system architectures such as two-tower, transformer-based models, and multi-task learning. - Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow. - Track record of building ML systems from 0→1 in ambiguous 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, with the ability to lead cross-functional technical initiatives and thrive in ambiguous, technically challenging problem spaces.

Nice to have

- Experience building internal ML platform tooling adopted by multiple product teams. - Familiarity with personalized marketing systems such as lifecycle targeting, audience segmentation, lookalikes, and campaign optimization. - Deep expertise in distributed training and large-scale data processing pipelines.

Benefits and work setup

- U.S.-based position that can be remote, with the option to work from a Bay Area office when local. - Base salary range of $272,000 to $340,000, plus equity and benefits, with compensation varying by role, level, skills, experience, and training.