Member of Technical Staff (Backend/Infrastructure Engineer, Search)

We are seeking an experienced Backend Software Engineer to join our Crawler team. In this role, you will design, develop, and operate systems that ingest, process, and manage web-scale data in support of our next generation of advanced search technologies. This is a critical, high-impact engineering position, requiring expertise across both backend and frontend components of our data acquisition stack. Responsibilities Take ownership of and lead projects focused on developing large-scale web crawlers, ingestion pipelines, and data processing systems. Build, maintain, and optimize core backend and frontend components for crawler services, including storage, retrieval, and UI dashboards for data management. Collaborate closely with Search and Infrastructure teams to ensure the reliable, high-quality ingestion and processing of billions of web pages. Architect and implement fullstack features and scalable distributed systems that handle high-load and real-time data operations. Rapidly iterate, experiment with novel approaches, and continuously enhance system performance, usability, and reliability. Qualifications Minimum of 5 years of software development experience, with strong knowledge of data structures and algorithms in at least one of the following languages: Python, C++, Rust, or Go. Experience with large-scale web crawlers is highly desirable. Proven experience building, deploying, and optimizing high-load, distributed, and hardware-adjacent services. Deep understanding of cloud infrastructure, with hands-on experience in Kubernetes (K8s) and AWS. Demonstrated passion for writing clean, efficient, and scalable systems.

Perplexity is seeking a DevOps engineer to join our small team in revolutionizing the way people search and interact with the internet. You will be responsible for leading the design, implementation, and scaling of the infrastructure, tooling and systems that support our Search feature. The ideal candidate should have experience in designing highly scalable infrastructure, building systems, tools and performing testing, monitoring, and maintenance. Our backend stack includes Python, Go, Rust, PostgreSQL, DynamoDB, Redis, and Kubernetes - built alongside dedicated in-house AI and search interfaces. Responsibilities: Design and implement highly available, high-performance, and scalable systems. Design and implement various tools for other engineers helping them with their day to day job. Design and implement CI/CD and other workflows to enhance stability, robustness and speed of iteration. Maintain and optimize key-value and relational databases. Scale and load balance web server backends to meet rapidly changing needs. Monitor systems and applications, proactively identifying and resolving reliability, scalability, or performance issues. Develop monitoring tools, alerts, and dashboards to provide visibility into system health and performance. Qualifications: Strong experience with cloud infrastructure built on AWS. Proficient in database management and caching strategies. Excellent problem-solving and troubleshooting skills, with the ability to analyze, debug, and resolve complex technical issues. Experience working with containers (Docker, Kubernetes) and orchestration tools. Excellent communication and collaboration skills. Experience with Python and Terraform. 3+ years of DevOps/SRE/System Administration experience.

Perplexity is looking for an experienced analyst to help us build and improve our core search technologies. You'll work at the intersection of data analysis and engineering - designing metrics, building data pipelines, and improving the quality of our search and answer systems. This role is hybrid in Belgrade, London or Berlin. Responsibilities Find and diagnose quality issues in our search pipeline Design metrics from scratch to track and measure search quality Build datasets for model training, including LLM-as-a-judge labeling pipelines Improve search snippet quality and page selection algorithms for indexing Design and analyze A/B experiments to validate improvements Qualifications 4+ years of experience as a data analyst or in a related role Strong coding skills — expected to write production-grade code at a mid-level backend engineer level Proficiency with SQL and Python Demonstrated hands-on experience with at least one of the following: Designing metrics from scratch (not just analyzing existing A/B experiments) Building labeling pipelines using LLM-as-a-judge Training ML models that shipped to production with measurable metric improvements Designing evals with known ground truth (e.g. SimpleQA, BrowseComp) or driving meaningful improvements on such evals Nice to have Experience working on search-related products Knowledge of statistics and A/B experiment design Experience with Apache Spark or Databricks

The internet is vast, containing trillions of URLs. Perplexity’s crawling and storage system is complex and has multiple stages (URL discovery, crawling, parsing, indexing). Each stage offers many opportunities for improvement and room for intricate bugs. You’ll work at the intersection of data analysis and engineering — designing metrics, building data pipelines, and improving the quality of our search and answer systems. Responsibilities Find and diagnose quality issues in our crawling pipeline Train small models that optimize particular aspects of the pipeline (e.g. parsing quality) Build datasets for model training, including LLM-as-a-judge labeling pipelines Improve page selection algorithms for indexing Design and analyze experiments to validate improvements Qualifications 4+ years of experience as a data analyst, ML engineer, or in a related role Strong coding skills: you should be able to write production-grade code at the level of a mid-level backend engineer Experience designing metrics from scratch Experience training ML models that shipped to production with measurable metric improvements Nice to have Direct experience working on web crawling or indexing pipelines

Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. Our work spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective. We control the full stack: the models, the agent harnesses, and the search infrastructure underneath. That gives us the freedom to develop new approaches across all three training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work. Responsibilities Push search and agent quality forward through improvements to models, training data, tools, and system design. Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion. Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively. Design and build agent harnesses around the tools, context management, execution environments, and orchestration that support reliable work over many steps. Improve retrieval and ranking models and the search interfaces agents use to find and assess information. Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements. Own experiments end to end, from a clear hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost. Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production. Qualifications A strong track record of building and shipping ML systems, with deep experience in one or more of LLM post-training, reinforcement learning, search and retrieval, or agent systems. Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems. Experience designing rigorous evaluations

Perplexity is seeking an experienced Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities Relentlessly push search quality forward—through models, data, tools, or any other leverage available Architect and build core components of our search platform and model stack Train and evaluate retrieval, ranking and classification models, including LLMs Deploy models - from boosting to LLMs - in a scalable and performant way Build and optimize RAG pipelines for grounding and answer generation Collaborate with Data, AI, Infrastructure and Product teams to ensure fast and high quality delivery Qualifications Deep understanding of search and retrieval systems, including quality evaluation principles and metrics Proven track record with large-scale search or recommender systems Self-driven, with a strong sense of ownership and execution Minimum of 5 years of working on search or recsys-related projects