Perplexity AI
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. Final offer amounts are determined by multiple factors, including, experience and expertise, and may vary from the amounts listed above. Equity: In addition to the base salary, equity
may be part
of the total compensation package. Benefits: Comprehensive health, dental, and vision insurance for you and your dependents. Includes a 401(k) plan. Create a Job Alert Interested in building your career at Perplexity AI? Get future opportunities sent straight to your email. Apply for this job
* indicates a required field First Name * Last Name * Email * Phone * Resume/CV * Enter manually Accepted file types: pdf, doc, docx, txt, rtf Enter manually Accepted file types: pdf, doc, docx, txt, rtf Website LinkedIn Profile Will you now or in the future require visa sponsorship for employment? * Select... Perplexity has an office-centric work model with 4 days per week in the office from the following locations: San Francisco, Palo Alto, or New York. Are you willing to come in 4 days per week? * Select... If you are not based in any of these locations, are you open to relocation to either San Francisco, Palo Alto, or New York? * Select... What are you looking for in your next role? * Why you think you will be a fit for Perplexity? * How do you use AI in your daily life and engineering work? * Describe a project you worked on recently about Personalization / Deep Learning / Information Retrieval Infrastructure that you were the most proud of? Tell us why. In order to build a best personalization product like Pinterest, Instagram and X, what you think is the most important thing to get it right? If you want to build a recommender system from scratch, how you plan to leverage LLM / Generative AI technologies to do it?
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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. Final offer amounts are determined by multiple factors, including, experience and expertise, and may vary from the amounts listed above. Equity: In addition to the base salary, equity
may be part
of the total compensation package. Benefits: Comprehensive health, dental, and vision insurance for you and your dependents. Includes a 401(k) plan. Create a Job Alert Interested in building your career at Perplexity AI? Get future opportunities sent straight to your email. Apply for this job
* indicates a required field First Name * Last Name * Email * Phone * Resume/CV * Enter manually Accepted file types: pdf, doc, docx, txt, rtf Enter manually Accepted file types: pdf, doc, docx, txt, rtf Website LinkedIn Profile Will you now or in the future require visa sponsorship for employment? * Select... Perplexity has an office-centric work model with 4 days per week in the office from the following locations: San Francisco, Palo Alto, or New York. Are you willing to come in 4 days per week? * Select... If you are not based in any of these locations, are you open to relocation to either San Francisco, Palo Alto, or New York? * Select... What are you looking for in your next role? * Why you think you will be a fit for Perplexity? * How do you use AI in your daily life and engineering work? * Describe a project you worked on recently about Personalization / Deep Learning / Information Retrieval Infrastructure that you were the most proud of? Tell us why. In order to build a best personalization product like Pinterest, Instagram and X, what you think is the most important thing to get it right? If you want to build a recommender system from scratch, how you plan to leverage LLM / Generative AI technologies to do it?
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