Staff Software Engineer - Backend

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 looking for an Infrastructure Engineer to own and improve the backend systems behind our latency-sensitive search stack. You’ll work across high-QPS Rust and Go services, distributed retrieval systems, cloud infrastructure, observability, and deployment tooling. This role combines infrastructure expertise with software engineering. You’ll take systems from design through production operation and use AI-native tools where they meaningfully improve engineering and operational workflows Responsibilities Build and operate scalable, low-latency infrastructure for search serving and retrieval. Design and improve distributed backend systems that handle high request volumes. Debug and optimize Linux systems, containers, networking, and production services. Improve reliability through observability, capacity planning, performance profiling, and incident response. Design, deploy, and operate cloud-native systems, primarily on AWS and Kubernetes. Build internal tools and automation for development, testing, debugging, and infrastructure operations. Improve CI/CD pipelines, release processes, and production rollout safety. Contribute directly to product codebases across Rust, Go, and other systems languages. Own systems end to end, from architecture and implementation to deployment and production operation. Requirements Strong experience with cloud infrastructure, distributed systems, and automation. Deep understanding of Linux internals, performance analysis, and production debugging. Experience building or operating latency-sensitive, high-throughput backend systems. Experience with containers, orchestration, observability, and infrastructure as code. Experience building or maintaining CI/CD systems and release tooling. Fluency in at least one systems language, such as Rust, Go, C++, or Java. Comfort working across infrastructure and application-level code. Strong ownership and the ability to operate effectively in a fast-moving environment. Nice To Have Experie

P-1416 At Databricks, we are passionate about helping data teams solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, Databricks has started a multi-year journey to build the best Data Intelligence Platform. For this we are building on top of a great foundation but our goal is to go further to build dramatically better products. From the Storage Engine (data layout, encryption, caching, ...) to the Query Engine (Vectorization, Query Optimization, …), we want to revisit every component to provide our customers with the fastest, easiest to use and most secure data platform for all their data workloads. As a software engineer, you will join as a founding member of the not only the Belgrade site, but really as a founding team for our multi-year journey to achieve our Lakehouse vision. You will be involved in the entire development cycle and exemplify all core Databricks values (own-it, data decide, teamwork, customer-obsessed). The responsibilities you will have : Drive requirements clarity and design decisions for ambiguous problems Produce technical design documents and project plans Develop new features Mentor more junior engineers Test and rollout to production, monitoring. What we look for: BS in Computer Science or equivalent practical experience in databases or distributed systems Comfortable working towards a multi-year vision with incremental deliverables Motivated by delivering customer value and impact 8+ years of production level experience in either Java, Scala or C++ Solid foundation in algorithms and data structures and their real-world use cases Experience with distributed systems, databases, and big data systems (Apache Spark™, Hadoop) About Databricks Databricks is the Data and A

P-1416 Our mission at Databricks is to radically simplify the whole data lifecycle from ingestion to ETL, BI, and all the way up to ML/AI with a unified platform. To achieve this goal, we believe the data warehouse architecture as we know it today will be replaced by a new architectural pattern, Lakehouse ( CIDR 2021 paper ), open platforms that unify data warehousing and advanced analytics. This new architecture will help address several major challenges, including data staleness, reliability, total cost of ownership, data lock-in, and limited use-case support. To achieve this vision, Databricks is building the next generation (decoupled) query engine and structured storage system that can outperform specialized data warehouses in relational query performance. Yet we will retain the expressiveness and robustness of general purpose systems such as Apache Spark™ to support diverse workloads ranging from ETL to data science. You will be part of this multi-year journey. As part of this team, you will design these next-generation systems that leapfrog state-of-the-art within the following areas: Query compilation and optimization Distributed query execution and scheduling Vectorized execution engine Data security Resource management Transaction coordination Efficient storage structures (encodings, indexes) Automatic physical data optimization The responsibilities you will have: Drive requirements clarity and design decisions for ambiguous problems Produce technical design documents and project plans Develop new features Mentor more junior engineers Test and rollout to production, monitoring. What we look for: A passion for database systems, storage systems, distributed systems, language design, or performance optimization Are comfortable working towards a multi-year vision with incremental deliverables Be customer-oriented and focused on having an impact 7+ years of experience working in a related system Optional: PhD in databases or distributed systems About Dat

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.