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

Precision Labs; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Perplexity Computer is building agent capabilities that transform knowledge into action. You will evaluate frontier models, design scalable agent systems, and own the lifecycle from prototyping to production launch, monitoring, and iteration.

Join a team that shapes how users and agents solve complex tasks with secure, observable architectures and a strong emphasis on craftsmanship, ownership, and rapid delivery.

Qualifications

• Typically 6+ years of professional software engineering experience.
• Strong software engineering fundamentals with AI/ML products or distributed systems.
• Experience owning the AI product lifecycle, data analysis, evaluation, and production monitoring.
• Practical experience in agent harnesses, tool use, context engineering, model evaluation, browser automation, or long-running task execution.
• Strong product judgment and ability to ship durable AI/ML solutions with measurable impact.
• Genuine interest in frontier AI capabilities and agent systems.

Responsibilities

• > Evaluate frontier models against real user tasks and turn advances into production agent systems.
• > Improve agents’ planning, tool use, context handling, error recovery, and long-running task reliability.
• > Apply state-of-the-art ML/LLM techniques to design scalable agent capabilities and architectures.
• > Own agent behavior end-to-end, from user interfaces to backend services and monitoring.
• > Build secure, observable agent systems with tracing and safeguards.
• > Collaborate with PM, Data Science, and Research to identify high-impact opportunities.

Skills

Software engineering

AI/ML products

Agent harnesses

Tool use

Context engineering

Model evaluation

Browser automation

Long-running tasks

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.