Employer description & source details
Own the Ranking Stack End-to-End: Design, build, and productionalize deep neural ranking models — including DCN-V2, MMoE, and Two-Tower architectures — operating at high throughput and low latency in production. You will own the full lifecycle: from offline experimentation and evaluation to monitoring, and iterative improvement. Drive Agentic ML Research: Embrace and champion the shift from manual experimentation to prompt-driven orchestration. You will leverage LLM coding agents (e.g., Claude Code, Gemini) to autonomously iterate on feature engineering and Learning to Rank (LTR) architectures. Inspired by the auto-research paradigm, you will design overnight experiment pipelines and review the outputs of hundreds of autonomous runs to identify signals quickly. Ensure Production Reliability: Maintain rigorous standards for model health in production. You will own monitoring for model drift, latency degradation, and feature pipeline integrity, and act quickly when signals deviate — keeping our ranking quality high for users across all markets. Collaborate Across Disciplines: Work as a technical partner with Backend Engineers, Data Engineers, and Product Managers to deliver end-to-end improvements. Translate complex ML trade-offs into clear narratives for non-technical stakeholders, and contribute to shaping the team's roadmap. Deep Learning Ranking & LTR: Hands-on, production-proven experience implementing modern deep learning ranking architectures — DCN-V2, MMoE, Two-Tower — with strong command of multi-task learning, cross-feature interactions, and embedding optimization at scale. Solid foundation in LTR methods (pointwise, pairwise, listwise), offline evaluation metrics (NDCG, MRR), and the challenges of bridging offline metrics to online A/B outcomes. Technical Depth
Extracted skills: python, sql, aws, gcp, machine learning