Member of Technical Staff (Applied AI Engineer, Agent Capabilities)

Join our team as a Member of Technical Staff (Applied AI Engineer, Agent Capabilities). In this role, you will be responsible for turning frontier AI breakthroughs into reusable product capabilities. You will evaluate emerging model capabilities, determine their user value, and transform them into reliable, scalable, high-quality experiences. This position offers broad ownership at the intersection of frontier AI research, agent systems, platform engineering, and product innovation.

• Evaluar modelos de vanguardia en relación con tareas reales de los usuarios, identificar comportamientos útiles y modos de falla, y convertir los avances más prometedores en sistemas de agentes de producción.
• Mejorar la capacidad de los agentes para planificar, utilizar herramientas, gestionar el contexto, recuperarse de errores y completar tareas de larga duración de manera confiable.
• Colaborar estrechamente con PM, Ciencia de Datos, Investigación, para identificar oportunidades de alto impacto en la comprensión y validación de las capacidades emergentes del modelo.

• Strong product judgment and execution: you can translate ambiguous user needs into applied AI or ML problems and ship durable solutions with measurable user impact
• Experience owning the AI product lifecycle, including data analysis, rigorous evaluation, production monitoring, and iterative improvement. Able to define metrics and use production data and user feedback to guide decisions
• Genuine interest in frontier AI capabilities, agent systems, and excitement for rapidly exploring, evaluating, and productizing new model behaviors
• Typically 6+ years of professional software engineering experience, with a track record of building and owning robust AI-powered, large-scale, user-facing or data-intensive products. Exceptional candidates with less experience and an outstanding record of impact are encouraged to apply
• Strong software engineering fundamentals, with experience building and operating AI/ML products, backend services, or distributed systems at scale
• Practical experience in one or more relevant areas, such as agent harnesses, tool use, context engineering, model evaluation, browser automation, or long-running task execution
• AI/ML research experience demonstrated through publications, open-source contributions, or other meaningful research impact
• Time spent at a fast-growing startup or on a high-ownership engineering team
• Experience with mid-training, post-training, or reinforcement learning for frontier or open-source models, along with a strong understanding of model strengths and limitations across reasoning, tool use, context management, and long-horizon tasks
• Experience building agent permissions, safeguards, evaluation infrastructure, or production observability systems
• Deep familiarity with the strengths and limitations of current model families across reasoning, tool use, context management, and long-horizon tasks
• Experience with LLM context engineering or harness engineering, experience with subagents, coding assistants, long-running or autonomous task execution