Full Stack Software Engineer - Applied AI

Perplexity • Palo Alto, CA

1d

About The Position

Perplexity is seeking full stack applied AI engineers to revolutionize the way people search and interact with information online by building next generation AI products and frontier models. In this role, you'll build scalable and innovative E2E solutions which powers Perplexity’s wide span of AI applications, including discovery, research agents, eval platforms, monetization and integration with different 3rd party and Sonar LLMs.
Our tech stack includes Python, Go, Rust, TypeScript, FastAPI, PostgreSQL, Redis, Docker, vLLM, and AWS.
Roles and teams at Perplexity are fluid. By applying to this position, you will be eligible to join teams across Perplexity Engineering. During the interview process, we look forward to learning more about your unique talents and figuring out where in our organization you’ll grow and thrive the most.

Requirements

• Hands-on experience building production AI or agentic applications end to end, including tool calling, context engineering, and integration with modern LLM APIs.
• Strong backend and systems design skills, with a track record of making practical tradeoffs between cost, latency, reliability, and quality in real-world systems.
• Proficiency with modern web or product stacks (e.g., Python plus a frontend framework) and comfort working across the stack from data and infra to UI.
• Self-directed and high agency, with 4+ years of industry experience in software or AI engineering and a bias toward shipping impactful product work.

Responsibilities

• Design and deliver scalable, end-to-end AI features that power core user experiences such as discovery, deep research, and monetization across Perplexity’s products.
• Build and evolve full stack systems that integrate multiple third-party and in-house Sonar models, ensuring seamless orchestration, robustness, and observability in production.
• Develop and maintain evaluation and experimentation platforms to measure answer quality, model performance, and product impact, supporting both online and offline workflows.
• Collaborate closely with research, product, design, and infra teams to translate frontier model capabilities into shipped user-facing features and internal tools.
• Continuously improve performance, reliability, and cost efficiency of AI-powered applications through thoughtful system design, instrumentation, and iteration.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed