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

Apply; New York, Northern (NY, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Perplexity Computer is building the next generation of agentic AI products. The Agent Capabilities team shapes frontier AI breakthroughs into reusable, high-quality experiences for users and agents.

You will own end-to-end agent behavior, leverage tools, and drive large-scale production systems across platforms. We value craftsmanship, ownership, and entrepreneurship, with a focus on measurable impact and rapid iteration in a highly collaborative environment.

Qualifications

• 6+ years of professional software engineering experience building AI-powered, large-scale products.
• Experience owning the AI product lifecycle from data analysis to production monitoring.
• Ability to translate user needs into durable AI/ML product solutions.

Responsibilities

• Evaluate frontier models against real tasks and turn advances into production agent systems.
• Improve agents’ planning, tool use, context management, and long-running task handling.
• Apply state-of-the-art ML/LLM techniques to design scalable agent capabilities and architectures.
• Own agent behavior end-to-end, set metrics, and drive iterative improvements.
• Build secure, observable agent systems with tracing and monitoring for reliability.
• Collaborate with PM, Data Science, and Research to identify high-impact opportunities.

Skills

Software engineering fundamentals

AI/ML products

Backend services

Distributed systems

Data analysis

Production monitoring

Metrics-driven decisions

Cross-functional collaboration

Tools

Python

Go

Rust

PostgreSQL

DynamoDB

AWS

TypeScript

Job description

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 is responsible for turning 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. This is a highly leveraged role with broad ownership at the intersection of frontier AI research, agent systems, platform engineering, and product innovation.

Tech Stack: Python | Go | Rust | PostgreSQL | DynamoDB | AWS | TypeScript

Why Perplexity is different

• Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious.

• Ownership. You identify the problem, design the solution and ship it.

• Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users.

• Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products.

• Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence.

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 integrate and use, auto-research, and multi-agent collaboration. Shape the architecture, abstractions, and product experiences that enable both users and agents to compose increasingly sophisticated solutions for real-world tasks.

• 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. Iteratively improve across models, prompts, harnesses, and products for different problem spaces.

• 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, Research, to identify high-impact opportunities in understanding and validating emerging model capabilities, and turn complex agent behaviors into simple, reliable product experiences.

• Apply relevant advances in models, inference, evaluation, and agent architecture when they produce measurable improvements in production performance. 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. 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.

• 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.

• 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: you can translate ambiguous user needs into applied AI or ML problems and ship 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.

Nice to have

• Experience with LLM context engineering or harness engineering, experience with subagents, coding assistants, long-running 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, along with a strong understanding of model strengths and limitations across reasoning, tool use, context management, and long-horizon tasks.

• 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.