• Explore and articulate how AI agents should use search, including new interaction patterns, query formulations, and evaluation criteria beyond traditional keyword relevance • Conduct deep applied research on search quality, focusing on moving from “textual match” toward answering the underlying question or intent • Train ranking and reranking models that optimize for answering intent and efficient resolution of agent queries, rather than purely textual similarity. • Design, prototype, and evaluate ranking and reranking approaches, including neural and LLM-based methods • Work on semantic retrieval and embedding-based ANN systems, including model selection, adaptation, and tradeoffs between recall, precision, and latency • Define and evolve quality metrics and evaluation methodologies appropriate for agentic search and question answering • Collaborate closely with backend and ML engineers to turn research ideas into production-ready components • Partner with product managers to connect research insights with product strategy and roadmap decisions • Serve as a technical thought leader for applied AI topics in the team, and help set direction for future research-oriented hires

• Design and evaluate training optimization techniques for large models (e.g. optimization algorithms, schedulers, normalization, curriculum strategies) • Improve training efficiency and stability across long runs and large datasets • Research and implement methods such as: • Optimizer and scheduler innovations • Mixed-precision, low-precision, and memory-efficient training • Gradient noise reduction, scaling laws, and convergence analysis • Training-time regularization and robustness techniques • Run large-scale experiments, analyze results, and translate findings into actionable improvements • Author or co-author research papers, technical reports, or blog posts • Collaborate closely with infrastructure and inference teams to ensure training decisions translate to real-world performance

• Research and develop techniques to optimize inference performance for large neural networks. • Improve latency, throughput, memory efficiency, and cost per inference. • Design and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications). • Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization). • Benchmark inference workloads across hardware accelerators. • Collaborate with engineering teams to deploy optimized inference pipelines. • Translate research insights into production-ready improvements.

• Design and execute research on multilingual datasets, including data collection, filtering, deduplication, and quality measurement • Develop strategies for low-resource and long-tail languages (sampling, augmentation, curriculum design) • Research and improve cross-lingual transfer, alignment, and robustness in large language models • Build and maintain evaluation benchmarks for multilingual performance • Collaborate with engineers and researchers on training pipelines and model architecture decisions • Publish research at top venues (e.g., ACL, EMNLP, NeurIPS, ICML, ICLR) and contribute to open-source when appropriate • Translate research insights into practical improvements in production models