• Design and train advanced ML models (e.g., DNNs, transformers) to power Reddit Ads Ranking models. • Develop and optimize features including embeddings, contextual signals, and cross-session behavior. • Own experimentation strategy: offline metrics, A/B design, readouts, and “next iteration” planning. • Work closely with product, infra, and data teams to drive end-to-end model deployment and performance analysis. • Mentor other MLEs and contribute to modeling best practices across the org. • Shape the long-term modeling vision across one or more domains (conversion, app ads, shopping, brand, etc.).

• Architect and significantly influence ML training/serving systems and tooling that unlock faster iteration and larger models. • Drive adoption and reliability of ML systems and own a portfolio of initiatives across multiple teams. • Improve efficiency: GPU utilization, training runtime, data loading, feature performance, and serving latency. • Write high-quality design docs; run design reviews; set standards for correctness, reliability, and velocity. • Partner cross-org on shared infra sequencing, requirements, and adoption. • Mentor other engineers and contribute to engineering best practices across the org.

• Design, train, and deploy a wide range of AI/ML models for mission-critical applications, ensuring they meet performance and scalability requirements. • Implement and manage automated MLOps pipelines for model monitoring, retraining, and lifecycle management to ensure continuous delivery and reliability. • Optimize model performance, scalability, and resource consumption in production cloud and on-premise environments. • Collaborate with data scientists, software engineers, and systems architects to translate model prototypes into hardened, production-grade solutions. • Uphold software engineering best practices, including robust version control, comprehensive testing, and CI/CD processes. • Stay current with industry trends and best practices in MLOps and operational AI to continuously improve the team's capabilities.

• Define and drive the machine learning technical strategy for Spotify Home, spanning retrieval, ranking, page composition, layout optimization, and real-time personalization • Work at the intersection of recommender systems, generative models, and user intent understanding to deliver highly adaptive and engaging Home experiences • Lead hands-on development of ML models and systems, from prototyping new ideas to productionizing solutions at global scale • Provide senior-level technical leadership across multiple teams, influencing architecture, modeling choices, and long-term investments • Design, build, evaluate, ship, and iterate on large-scale ML systems that directly power Home for hundreds of millions of users • Partner closely with product, design, and user research to translate UX goals and user needs into robust ML systems • Drive best practices in experimentation, offline/online evaluation, model lifecycle management, and reliability • Help evolve Home toward more contextual, generative, and intent-aware experiences, leveraging transformers, sequence models, and emerging techniques • Collaborate with platform and foundation teams to effectively leverage shared models and infrastructure while tailoring solutions to Home’s unique needs • Mentor senior engineers and influence technical standards through thoughtful reviews, documentation, and architectural guidance • Act as a technical ambassador for Home within Spotify’s broader ML community, staying current with research and industry trends