• You'll build the intelligence layer that powers that mission. That means working with 500M+ labeled blockchain addresses, training models on data no one else has, and shipping AI/ML systems that help real investors make better decisions and grow their portfolios. • Design and build AI/ML models on top of the richest onchain dataset in the world — then make them fast, scalable, and production-ready • Work shoulder-to-shoulder with engineers and product to ship models that actually reach users • Stay ahead of the curve in both AI/ML and blockchain — this space moves fast, and we need someone who moves faster • Level up the team: mentor junior engineers, raise the bar on what good looks like • Own your work end-to-end; from data pipeline to deployed feature

• Lead a broad portfolio of end-to-end initiatives in multimodal LLM and audio algorithms to achieve Netflix’s ambitious localization goals. • Mentor, support, and inspire a team of Research Scientists and Machine Learning Engineers. • Partner with technical leads on defining area strategy, planning and executing projects, and developing talent. • Set and operationalize a high bar for execution speed and quality. • Build strong relationships with cross-functional partners to shape long-term collaborations and shared visions. • Act as a functional leader and domain expert.

• Design and implement machine learning features used in production customer workflows. • Help identify and close feature and behavior gaps between our ML capabilities and common frameworks (e.g., Spark ML, scikit-learn). • Proactively evaluate semantic differences, defaults, and edge cases that could surprise customers. • Partner with product, architects, and customer-facing teams to anticipate upcoming customer needs and gaps. • Investigate and resolve issues where ML behavior diverges from user expectations (e.g., model output, metrics, configuration semantics). • Contribute to other ML initiatives including new models, metrics, performance improvements, and infrastructure work. • Analyze and improve the performance of existing ML code, balancing correctness and stability with customer facing latency. • Write clear design docs, tests, and documentation to make behavior explicit and prevent regressions.

• Collaborate with Agentic AI Scientists to build and securely deploy AI agents to automate and optimize labor intensive workflows • Support both R&D tasks and direct customer engagements to speed the transition delivery of novel applied research solutions onto direct contracts • Design, implement, and maintain tools that enable agent deployments using MLOps best practices in scalable cloud infrastructure • Develop and document processes that enable secure automated development and deployment of AI agents • Design, build, train, and evaluate Machine Learning models • Build repeatable Machine Learning pipelines for model training, evaluation, deployment, and monitoring • Perform R&D to enable AI Observability and performance metrics • Design, implement, and manage cloud resources for MLOps infrastructure • Operationalize production AI/ML systems by implementing model serving, monitoring, data and model drift detection, logging, and lifecycle management to ensure reliability, scalability, and maintainability • Work in a team of AI/ML researchers and engineers using Agile development processes