AI Engineer - Personalization Infrastructure

Perplexity is an AI-powered answer engine founded in December 2022 and growing rapidly as one of the world’s leading AI platforms. Perplexity has raised over $1B in venture investment from some of the world’s most visionary and successful leaders, including Elad Gil, Daniel Gross, Jeff Bezos, Accel, IVP, NEA, NVIDIA, Samsung, and many more. Our objective is to build accurate, trustworthy AI that powers decision-making for people and assistive AI wherever decisions are being made. Throughout human history, change and innovation have always been driven by curious people. Today, curious people use Perplexity to answer more than 780 million queries every month–a number that’s growing rapidly for one simple reason: everyone can be curious. 

Perplexity is seeking experienced AI Infrastructure Engineers to build the next generation of personalization infrastructure. In this role, you will develop cutting-edge systems that give our AI the ability to remember contexts, understand user intents, and deliver highly personalized experiences.

Responsibilities

• Architect and ship the next generation of personalization infrastructure: long‑lived LLM memory, long‑context understanding, conversation retrieval, async task execution, and related query retrieval
• Build event-to-embedding pipelines and feature stores; design vector and hybrid indices; implement retrieval/ranking services that power real‑time, personalized experiences.
• Partner cross‑functionally with Product and Research to translate signals into durable memory and measurable relevance lift; instrument metrics, run A/B experiments, and iterate quickly.
• Drive end‑to‑end ownership: data modeling, schema and contracts, batch/stream processing, model training, deployment, observability, and on‑call for owned services.

Qualifications

• Strong programming skills in production systems with hands‑on experience across data, serving, and infra layers.
• Proven experience building large‑scale retrieval and indexing infrastructure (vector/hybrid search, inverted indexes).
• Deep familiarity with LLM‑centric IR: RAG architectures, semantic retrieval, prompt/context optimization, and long‑context strategies.
• Experience building and deploying ML for personalization: retrieval/ranking models, feature engineering, offline evaluation, and online experimentation (A/B, interleaving).
• Ownership mindset, bias to action, and ability to deliver in fast‑moving, ambiguous environments.
• 4+ years of industry experience in distributed systems, search/recommendation, or ML systems. Experience with LLM memory or conversation retrieval is a strong plus.

The cash compensation range for this role is $200,000 - $280,000.