Manager I, Engineering - Applied AI - Natural Language & Conversational Interfaces

Who You Are

• 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

What You'll Do

• Lead and develop a team of engineers and applied scientists focused on building the foundations for agents operating at scale
• 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