• Build and ship predictive models on large-scale behavioral and event data - predicting engagement, finding the best time and audience for each message, scoring list health, and discovering customer segments • Fine-tune LLMs on our own data and outcomes to power a goal-driven assistant that recommends and takes action on a customer's behalf • Design and own the training and inference pipelines behind these models - data prep, training, evaluation, and serving • Build evaluation harnesses that prove a model is genuinely better before it ships - measuring real-world lift, not just offline metrics • Enforce reliable, structured model outputs so predictions and actions can be trusted in production • Collaborate with product and engineering teams who consume your models as shared infrastructure

• ML Engineer with 5-7 years of IT experience. • Pipeline Training Models, Building, Deployment, Testing, and Monitoring using AWS SageMaker, AWS CFT, AWS CodePipeline, Lambda, etc. • Develop Airflow DAGs to run training and scoring pipelines • Develop a Testing framework with Pytest • Implement monitoring solution with homebrew solution using Lambda and Dash • Develop Data Quality solutions potentially leveraging Great Expectations.

• Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale. • Deploy and manage machine learning & data pipelines in production environments. • Work on containerization and orchestration solutions for model deployment. • Participate in fast iteration cycles, adapting to evolving project requirements. • Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. • Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. • Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment. • Manage and monitor machine learning infrastructure, ensuring high availability and performance. • Implement robust monitoring and logging solutions for tracking model performance and system health. • Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance. • Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner. • Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations. • Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization. • Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

• Architect the ranking backbone unifying query understanding, personalization, multi-objective ranking, ads, and merchandising • Design and build a search autosuggest system • Design long-horizon objective functions and build uplift/causal value models • Develop production-grade Multi-Task Learning to jointly learn relevance, propensity, margin, and churn risk • Own the inference layer with goal-aware re-rankers and optimization • Advance evaluation practices for incremental GTV and retention • Partner across ads, infrastructure, product, and design teams • Mentor ML engineers to build expertise