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Role Description

This role is focused on architecting and scaling the ranking systems that power search, recommendations, and personalization across a high-traffic e-commerce platform.

• Design multi-task, multi-objective models that optimize for long-term value, relevance, and user engagement, while leveraging LLMs to enhance features and recall.
• Partner closely with engineers, product managers, and data teams to lead the development of production-grade ML systems.
• Ensure low-latency serving and mentor other ML engineers.
• Combine cutting-edge research with practical implementation, influencing user experience, revenue, and retention.
• Contribute to both technical strategy and operational excellence in large-scale machine learning systems.

• Architect and implement the ranking backbone that unifies search, personalization, ads, and merchandising into a single adaptive platform.
• Design and develop multi-task learning models (e.g., shared encoders, MMOE/PLE task heads) to jointly learn relevance, propensity, margin, and churn risk.
• Build value-aware, long-horizon objective functions and uplift/causal models to optimize incremental revenue, retention, and user engagement.
• Own low-latency inference pipelines including re-ranking, diversity and quality constraints, and safe exploration strategies.
• Advance evaluation practices through online experiments, counterfactual analyses, and attribution pipelines to measure long-term impact.
• Collaborate with cross-functional teams including product, ads, infrastructure, and design to translate business goals into ML policies and measurable ROI.
• Mentor and guide ML engineers, fostering expertise in ranking, causal inference, and scalable serving systems.

Qualifications

• 5+ years of experience applying ML at scale, with at least 3 years in technical leadership roles improving ranking or recommendation systems.
• Proven experience with multi-objective or constrained optimization to balance relevance, revenue, margin, and user experience.
• Strong coding skills in Python and data fluency using SQL/Pandas; experience with XGBoost and deep learning frameworks such as TensorFlow or PyTorch.
• Solid understanding of low-latency serving architectures, feature stores, caching, vector/lexical retrieval, and re-ranking systems.
• Expertise in multi-task learning, calibration, counterfactual evaluation, uplift/causal modeling, or contextual bandits is preferred.
• Hands-on experience leveraging LLMs for feature enrichment, long-tail recall, or reasoning-rich context in ML pipelines.
• Excellent analytical, problem-solving, and cross-functional communication skills.
• Experience with remote-first collaboration and asynchronous alignment across teams and time zones.

Benefits

• Competitive base salary, with ranges depending on U.S. location: $173,000–$219,000.
• Equity grants for new hires and annual refresh grants.
• Comprehensive medical, dental, and vision coverage.
• Flexible PTO and remote-first work culture.
• Opportunities for mentorship, professional development, and research contributions.
• Access to cutting-edge ML infrastructure and projects impacting millions of users.
• Inclusive and collaborative environment with a focus on innovation and learning.