Machine Learning Engineer

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

AI Engineer

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Work with business and operational teams to understand the problem, the operational and system constraints, the available data, and decision points.
• Define how opportunities fit within the long-term evolution of the systems.
• Based on the problem and the available data, determine how we can measure success and monitor system outcomes/dynamics.
• Define and drive the evolution of the solution, identifying points of high leverage, and phases/evolution of the system.
• Evolve plans and direction as we iteratively gather feedback and learn about solution effectiveness.
• Collaborate with cross-functional leaders to identify opportunities, communicate and evaluate solutions, and gather feedback.
• Mentor of data scientists and analysts, fostering a collaborative and high-performing environment.
• Provide technical guidance, set standards, and ensure the team uses appropriate tools and methodologies.
• Stay current with emerging trends in data science, AI, and big data technologies to drive innovation and maintain a competitive advantage.

Principal Machine Learning Revenue Architect

• Work on high-impact problems at the intersection of LLM research, large-scale ML systems, and real-world e-commerce applications.
• Choose to work in areas like query understanding, search relevance and ranking, generative recommendations, LLM evaluation, and more.

Machine Learning Engineer, PhD Intern

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

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Senior Machine Learning Engineer II, Search & Recommendations Ranking

Senior Software AI Engineer

AI Engineer

Principal Machine Learning Revenue Architect

Machine Learning Engineer, PhD Intern

Senior Software Engineer II, ML/AI Platform