Manager I, Applied AI - Distilled Models
Leads engineers and applied scientists building cost-efficient specialized AI models and security capabilities from research through production. The role requires people leadership, deep AI/ML expertise, strong evaluation practices, and product judgment for taking AI initiatives from 0 to 1.
About the job
Responsibilities
- Lead and develop a team of engineers and applied scientists focused on cost-efficient specialized models and AI security capabilities.
- Work with product managers, research teams, and cross-functional partners to shape initiatives from initial framing through broader adoption, defining success criteria at each stage.
- Own end-to-end delivery of AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality.
- Balance quality, latency, cost, and safety considerations when shipping AI-powered products.
- Drive evaluation and iteration practices for AI systems, including defining quality standards and guiding the development of offline and online evaluation pipelines to measure quality and detect drift.
- Contribute to cross-team collaboration and knowledge sharing across the broader AI organization.
- Support engineers' career growth through coaching and feedback, foster experimentation and learning, participate in hiring, and help shape the future team.
Requirements
- Experience leading and mentoring engineers and developing strong engineering talent in a fast-moving domain.
- Deep expertise in one or more areas of AI or machine learning, such as large language models, retrieval-augmented generation, semantic search, agentic systems, deep learning, or natural language processing.
- Familiarity with AI system evaluation methodologies, including offline benchmarks and online metrics.
- Strong product judgment and the ability to anchor early-stage work in customer problems, define success criteria, and shape product direction with product and research partners.
- Experience taking AI products from 0 to 1 is strongly valued, including scoping hypotheses, moving quickly toward signal, and deciding what to pursue, pivot, or stop.
- BS, MS, or PhD in Machine Learning, Computer Science, Engineering, or a related field, or equivalent professional experience.
Benefits
- Hybrid workplace designed to support collaboration and work-life harmony.
Skills
Artificial Intelligence, Machine Learning, LLMs, Retrieval-Augmented Generation, Semantic Search, Agentic Systems, Deep Learning, Natural Language Processing, Ai Evaluation, Offline Benchmarks, Online Metrics, Evaluation Pipelines, Ai Security, System Reliability, Product Management
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