• Lead and scale high-impact research across product development, brand strategy, and marketing. Independently conduct complex generative and evaluative studies that uncover unmet needs, inform product vision, and de-risk decisions at scale. • Implement advanced AI-native practices and embed AI into research workflows to ensure speed, quality, and impact. Define what it means to work AI-natively across research internally and for our customers. Stay up to date on the latest AI-based research techniques and tools. • Connect the dots, patterns and connections across teams. Ensure insights are shared across teams and inform decisions. Partner with senior leadership to identify and evangelize the highest-impact opportunities for our customers. • Be the in-house expert across all core research methods, including usability testing, ethnographic research, surveys, segmentation, message testing, brand tracking, and concept validation. • Coach designers, marketers, product managers and other employees on conducting research, reviewing their projects and adapting the AI first workflow based on needs • Lead and moderate company-wide Voice of the Customer (VoC) sessions, interviewing real customers in live forums to bring their perspectives directly to cross-functional teams • Manage external research contractors and vendors, including scoping, quality control, and budget alignment • Build and scale a research enablement model, coaching designers, marketers, and product managers to run self-serve research with confidence and consistency. Develop tools, templates, and guidance that scale research capabilities and impact. • Collaborate with cross-functional leadership to build and prioritize a unified, impact-driven research roadmap • Be a skilled communicator to peers to executives. Articulate effective customer stories to drive change and inform strategy of product initiatives to business strategy.

• 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.