Member of Technical Staff (Machine Learning Engineer, Search)

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 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 improve search quality across the middle and later stages of ranking. We are looking for a strong ranking generalist who can own ambiguous problems end to end and brings exceptional depth in either neural ranking or production ranking systems. Responsibilities Push search quality forward through models, data, evaluation, infrastructure, or any other leverage available. Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely. Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate. Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring. Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity. Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome. Qualifications Deep understanding of search or recommender systems and their evaluation. Proven ownership of a large-scale production ranking system or a substantial class of quality problems. Strong machine learning and software engineering skills across data, models, serving, and monitoring. Ability to drive ambiguous, cross-team problems without continuous task decomposition. Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.

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

Perplexity is looking for a highly skilled Expert Systems Engineer to join our Search Core team. For the indexing and data processing stream. This role is critical to building next-generation search products and technologies. Responsibilities Design and build core search engine components, especially indexing pipelines, data storage formats that operate at the scale of billions of pages Develop streaming and batch data processing with YT systems for search index construction in a high-load environment Push the limits of hardware performance through low-level optimizations and systems-level tuning Tackle challenges in multithreading, concurrency, and system-level optimization Design and build core search engine components, including indexing pipelines, retrieval algorithms, and ranking systems that operate at the scale of billions of pages Develop streaming and batch data processing systems for search index construction in a high-load environment Push the limits of hardware performance through low-level optimizations and systems-level tuning Tackle challenges in multithreading, concurrency, and system-level optimization Qualifications 3+ years of hands-on experience in systems programming (Rust, C++, C, or similar) Ownership of full project lifecycle you don't just write a fast inner loop, you care about how the system is built, deployed, operated, and scaled in production Knowledge of Python or other scripting languages Knowledge of Data processing systems (YT/Kafka/Hadoop) Passion for writing clean, efficient, and scalable systems-level code Strong knowledge of algorithms and data structures, and the ability to apply them effectively Deep understanding of multithreading, including various approaches, challenges, and trade-offs Experience building high-load, distributed, and hardware-adjacent services Solid understanding of Linux internals (syscalls, networking stack, memory model, kernel tuning) Familiarity with low-level optimization techniques (memory management,

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.