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

About the Role

Perplexity Computer is one of the defining products of the new era of agentic AI. Millions of people use Perplexity to transform knowledge into action, and the Agent Capabilities team sits at the intersection of frontier AI research and product innovation, building the foundations that shape how users and agents solve increasingly complex tasks.

As every major breakthrough in AI models creates new possibilities, the Agent Capabilities team turns frontier AI breakthroughs into reusable product capabilities. We are often the first to evaluate emerging model capabilities, determine where they create real user value, and transform them into reliable, scalable, high quality experiences for both users and agents.

Tech Stack

Python | Go | Rust | PostgreSQL | DynamoDB | AWS | TypeScript

What You'll Do

• Evaluate frontier models against real user tasks, identify useful behaviors and failure modes, and turn the most promising advances into production agent systems. Own the lifecycle from rapid prototyping and evaluation through launch, monitoring, and 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, tools integration and use, auto-research, and multi-agent collaboration.
• Own agent behavior and capabilities end-to-end, from user-facing products and interfaces to backend services. Define offline and online 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. Develop tracing, replay, and monitoring infrastructure that makes agent failures reproducible and actionable.
• Collaborate closely with PM, Data Science, and Research to identify high-impact opportunities in understanding and validating emerging model capabilities.
• Set technical direction on ambiguous problems and raise the bar through design reviews, mentorship, and technical leadership.

Qualifications

• 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.
• 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: ability to translate ambiguous user needs into applied AI/ML problems and ship durable solutions.
• Genuine interest in frontier AI capabilities, agent systems, and excitement for rapidly exploring and productizing new model behaviors.

Nice to Have

• Experience with LLM context engineering, harness engineering, subagents, coding assistants, or autonomous task execution.
• Deep familiarity with the strengths and limitations of current model families across reasoning, tool use, context management, and long-horizon tasks.
• Experience building agent permissions, safeguards, evaluation infrastructure, or production observability systems.
• Experience with mid-training, post-training, or reinforcement learning for frontier or open-source models.
• AI/ML research experience demonstrated through publications, open-source contributions, or other meaningful research impact.
• Experience at a fast-growing startup or high-ownership engineering team.