Manager I, Applied AI - Edge Models

## Responsibilities - Lead and develop a team of engineers and applied scientists focused on cost-efficient specialized models and AI security capabilities - Work closely with product managers, research teams, and cross-functional partners to shape the team's bets from initial framing through to broader adoption, with a clear definition of success criteria at each stage - Own end-to-end delivery of high-quality AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality - Navigate the unique challenges of shipping AI-powered products: balancing quality, latency, cost, and safety considerations. Drive evaluation and iteration practices for AI systems: define the quality bar and guide the team in building the offline and online evaluation pipelines needed to measure quality and detect drift - Contribute to cross-team collaboration and knowledge sharing across the broader AI organization - Support career growth for engineers through coaching, feedback, and fostering a culture of experimentation, innovation, and learning. Participate in hiring and help shape the future team as the organization grows ## Requirements - A people-focused manager with experience leading and mentoring engineers, able to develop strong engineering talent in a fast-moving domain - A technical leader with deep expertise in one or more areas of AI or machine learning: large language models, retrieval-augmented generation (RAG), semantic search, agentic systems, deep learning, or NLP - Well-versed in evaluation methodologies for AI systems, both offline benchmarks and online metrics - A strong product instinct: able to anchor early-stage work in concrete customer problems, define success criteria before writing code, and actively contribute to shaping product direction alongside product and research partners - Experience taking AI products from 0 to 1 is strongly valued: able to bring structure to early-stage work by scoping clear hypotheses, moving quickly toward signal, and making deliberate decisions about what to pursue, pivot, or stop - BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field, or equivalent professional experience ## Nice to Have - Experience with AI security capabilities - Experience with hybrid workplace environments ## Benefits - Hybrid workplace environment - Opportunity to work on cutting-edge AI technologies - Collaborative and innovative team culture - Career growth opportunities - Cross-team collaboration and knowledge sharing