Principal Machine Learning Engineer

Role overview

This principal-level position bridges hands-on MLOps execution with research-oriented thinking, spanning everything from infrastructure setup to publication-grade experimentation. The role carries real latitude to shape a long-term vision for search and discovery, particularly as large language models reshape what is feasible to build internally. The successful candidate will translate model results into clear business and revenue impact while influencing architecture and statistical methodology across the engineering organization.

Responsibilities

- Build new ML systems from the ground up, close data gaps, and maintain and improve the existing ML pipeline. - Read and implement ML papers on alternative model architectures, then run training experiments to benchmark results against current production models. - Stand up and manage supporting infrastructure, such as Redis clusters, to handle ML workloads. - Weigh in on and help drive architecture decisions and statistical methodology alongside the broader engineering team. - Help define the long-term search and discovery vision, informed by how large language models have shifted what is possible to build. - Present model results and roadmap, including business and revenue impact, to broad or executive audiences.

Requirements

- 8+ years of machine learning engineering spanning both MLOps and research-oriented work, with demonstrated principal-level impact across the full loop from research paper to production implementation to business impact presentation. - Hands-on experience reading and implementing ML papers, and running comparative training experiments against production baselines. - Experience building and operating ML infrastructure and pipelines, including standing up supporting services like Redis clusters. - An informed perspective on search and discovery systems, particularly how large language models have changed the build-versus-buy calculus. - Comfort operating as a cross-functional driver, able to debate architecture and statistical methodology across teams rather than purely as an individual contributor. - Experience presenting technical results, roadmaps, and business or revenue impact to broad or executive audiences.

Nice to have

- Hands-on experience with search, personalization, recommendations, ranking, or lifecycle modeling. - Familiarity with MLOps best practices, including model versioning, CI/CD for ML, monitoring, and operating models in containerized and orchestrated environments such as Docker and Kubernetes. - Background in e-commerce, marketplace, or subscription-based businesses. - Experience defining and owning metrics, experimentation frameworks, or production model performance monitoring. - Demonstrated ability to influence beyond immediate project scope, shaping best practices, standards, or strategy across teams. - Comfort working in environments with moderate technical debt or evolving data foundations.

Benefits and work setup

- Base salary range of $175,000–$250,000, plus equity, with total compensation including stock options, health and wellness benefits, and flexible PTO. - Comprehensive medical, dental, vision, life, and disability coverage. - 401k employer match. - Subsidized wellness and fitness membership with access to fitness, wellness, and beauty experiences. - Coverage for life coaching and therapy sessions through a holistic mental health platform. - Employee discount on membership-based purchasing. - Requires twice-yearly travel to the Los Angeles, California headquarters for all-company summits, once in summer and once in winter. - Employees must be based in the United States as a condition of employment.