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

About the job

Responsibilities

• Evaluate frontier models against real user tasks, identify useful behaviors and failure modes, and turn promising advances into production agent systems, owning the lifecycle from prototyping to iteration.
• Improve agents' ability to plan, use tools, manage context, recover from errors, and complete long-running tasks reliably.
• Apply state-of-the-art ML and LLM techniques to design scalable agent capabilities such as skills, plugins, artifact generation, tool integration, auto-research, and multi-agent collaboration.
• Shape the architecture, abstractions, and product experiences that enable users and agents to compose sophisticated solutions for real-world tasks.
• Own agent behavior and capabilities end-to-end, from user-facing products to backend services, defining and iterating on evaluations for task completion, correctness, safety, latency, cost, and user satisfaction.
• Build secure, observable, and reliable agent systems, including permissions and safeguards for sensitive actions, and develop tracing, replay, and monitoring infrastructure.
• Collaborate with PM, Data Science, and Research to identify high-impact opportunities and translate complex agent behaviors into simple, reliable product experiences.
• Apply relevant advances in models, inference, evaluation, and agent architecture to improve production performance.
• Set technical direction on ambiguous problems and raise the bar through design reviews, mentorship, and technical leadership.

Requirements

• 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.
• Strong software engineering fundamentals, with experience building and operating AI/ML products, backend services, or distributed systems at scale.
• Experience owning the AI product lifecycle, including data analysis, rigorous evaluation, production monitoring, and iterative improvement.
• Ability to define metrics and use production data and user feedback to guide decisions.
• 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.
• Strong product judgment and execution, translating ambiguous user needs into applied AI or ML problems and shipping durable solutions with measurable user impact.
• Genuine interest in frontier AI capabilities, agent systems, and excitement for rapidly exploring, evaluating, and productizing new model behaviors.