• 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

• Design and implement ML pipelines (training, validation, deployment, and inference) with a focus on scalability and cost. • Deploy models to production using MLOps practices (versioning, reproducibility, automation, and performance/drift monitoring). • Build and maintain inference services (batch and/or real-time), APIs, and integrations with internal systems. • Collaborate with data scientists, analysts, and product teams to translate business needs into solutions. • Ensure quality (testing, code reviews, documentation), security, and engineering best practices. • Track operational and outcome metrics (SLA, latency, cost, accuracy, business impact).

• Design and build scalable ML services for enrichment workflows, including model training pipelines and high-performance inference APIs • Deploy and optimize models using modern inference libraries and frameworks (ONNX, vLLM, TensorRT, etc.) to achieve low-latency, high-throughput performance • Collaborate with software engineers and product teams to define data requirements, feature engineering strategies, and model evaluation metrics • Build robust monitoring, observability, and evaluation systems to ensure model quality and service reliability in production • Stay current with emerging ML techniques, tools, and best practices, particularly in areas like model optimization, efficient inference, and large-scale data processing