• 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

• Deploy and monitor ML systems in production, from classical NLP and embedding models to LLM-powered features • Own the evaluation stack - golden datasets, "model-as-a-judge" frameworks, inter-annotator agreement, and regression tests that gate releases • Build and maintain our vector embeddings ecosystem and the retrieval, classification, and similarity patterns that sit on top of it • Partner with Data Science on annotation workflows, PII scrubbing, and ground-truth pipelines • Improve our MLOps foundations - versioning, observability, drift detection • Translate fuzzy product problems into measurable AI features with clear success criteria

• Improve our existing machine learning systems using your core coding skills and ML knowledge • Identify new opportunities to apply machine learning to different parts of the Quora product • Work with other machine learning engineers to implement algorithms and systems efficiently • Take end-to-end ownership of machine learning systems - data pipelines, candidate extraction, feature engineering, model training, as well as integration into our production systems