Member of Technical Staff (General Software Engineer, Infrastructure)

By applying to this role, you will be considered for engineering roles across all teams at Perplexity. We are hiring builders to join our highly leveraged engineering team for creating new products that innovate and accelerate human productivity In 2026, we launched Computer, the defining product for the new era of agentic AI. We’ve scaled beyond the millions of people using Perplexity every day for research, shopping, investing and curiosity into a new paradigm of using AI to transform knowledge into action. As an engineer at Perplexity, your role will be to build products and systems that define how AI empowers humans to think, decide and act. You’ll ship fast and work between product, infrastructure and frontier level advancements in technology. Why Perplexity is Different Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious. Ownership. You identify the problem, design the solution and ship it. Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. Partnership. We amplify each others’ strengths, break down silos, and give selflessly to help our colleagues deliver excellence. What you'll do Design, build, and own product and platform systems for Computer Lead features, projects and products end-to-end, from problem definition to technical design, implementation, and launch. Hill climb on hard problems, continuously iterating to improve for ourselves and customers. Partner closely with engineers, product managers, designers, data scientists, and go-to-market teams. Build systems that take into account the nuances of AI, working with agents, context, evaluation, personalization and the ground truth. Qualifications 4+ years of professional software engineering experience. Strong experience in at least one general-purpose programming langua

APPLICATIONS CLOSED The general application portal has closed for 2026. We will reopen applications at some point in 2027. Thank you for your interest in Perplexity careers! Welcome! This is the general application portal for university students and recent graduates who wish to be considered for full-time Member of Technical Staff positions at Perplexity. Our elite new grad cohorts enjoy unparalleled mentorship, top-of-market compensation, and the chance to tackle some of the deepest, thorniest challenges in frontier intelligence . NOTE: if you attend one of our focus schools listed below, please do not apply via this portal as it is infrequently monitored due to high volume. Apply instead via the school-specific application portal below to ensure your application receives priority attention. Focus schools (United States) - application portals Caltech | Columbia | Cornell | CMU | Duke | Georgia Tech Harvard | MIT | Michigan | Northwestern | NYU | Penn Princeton | Purdue | Stanford | UC Berkeley | UChicago UCLA | UIUC | UNC Chapel Hill | UT Austin UWashington | Yale Focus Schools (Canada) - application portals McGill | UBC | UToronto | Waterloo Early Career Talent @ Perplexity ​Perplexity believes that exceptionally talented individuals at any stage can thrive in our high-velocity, high-ownership culture. ​Our early career talent program revolves around: ​ Breadth : New grads at Perplexity work across the stack, from Perplexity's agent harnesses to our AI inference and training systems. Many of our most beloved features and most important systems were shipped by individuals fresh out of school. ​ Depth : New grads are entrusted (and expected) to dive deep into whatever problem domains you work on. Often, your work will require you to become a world expert in some problem space that few others in the world have encountered. ​ Mentorship & Growth : We keep the size of our entering new grad cohorts at well below 10% of our technical team. Each individual enjoys a ri

Perplexity is seeking energetic researchers and engineers to join our Secure Intelligence Institute (SII), Perplexity's flagship research center for advancing security, privacy, and trust in frontier intelligence. SII’s goals are to advance frontier AI security research, translate those advances into concrete improvements in Perplexity's systems, and share knowledge and resources that strengthen the broader AI ecosystem. As a member of SII, you'll conduct original and impactful research on improving the security and privacy of frontier intelligence systems. Your goal will be to conduct research that is not only rigorous in theory, but practical enough to improve the systems people rely on every day. This work will be informed by the realities of operating general-purpose AI systems used by millions of people and thousands of enterprises, and you'll be expected to translate both your own research and advances from the broader community to practical improvements that protect and defend Perplexity's users. Responsibilities Develop threat models for emerging attack surfaces in AI-native products, including browser, search, and autonomous agents. Identify and analyze security and privacy threats across AI systems, infrastructure, and user-facing products. Develop novel defenses, mitigations, and detection mechanisms for security and privacy in AI-native products. Build security evaluation frameworks, benchmarks, and datasets to measure the effectiveness of different defense mechanisms. Partner with Perplexity’s Security Engineering team to translate state of the art research into shipped security features and hardened system architectures. Collaborate with top-tier academic and industry researchers in SII's external research network. Publish findings at premier venues and contribute to the broader security research community. Qualifications Hold a PhD (or equivalent research experience) in Computer Science, Computer Engineering, or a related field, with a primary focus o

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll set technical direction for the primitives that other teams (filesystems, training, sandboxes) build on, balancing durability, latency, throughput, and cost. You'll own the roadmap from today's hardest problems (garbage collection at petabyte scale, active-active replication, rate limiting that protects the upstream without wasting ut

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: Modal’s Inference Runtime team owns the container runtime stack used to run inference and training workloads across our fleet. We work at the boundary of Linux, containers, filesystems, storage, GPU drivers, and distributed systems.Our goal is to make demanding ML workloads start quickly, run efficiently, and remain securely isolated — whether they use a single GPU, hundreds of gigabytes of memory, or multiple GPUs connected with RDMA.We’re looking for a systems engineer who enjoys working deep in the stack. You’ll build production runtime infrastructure in Rust and Go, diagnose difficult Linux and performance problems, and help determine the architecture of Modal’s container platform. You’ll also work closely with maintainers of gVisor and contribute to the runtime itself when the right fix belongs upstream. The Work: Make container startup, checkpoint, and restore dramatically faster for large i

About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. About the Role As a Member of Technical Staff, you'll join one of four engineering teams building the systems behind Decagon's AI agents: Agent Engineering, Agent Orchestration, Enterprise Product, or Infrastructure. Engineers here own their work end to end and are trusted to make a real impact from the start. This role is for someone who dives deep into complex system challenges and builds elegant solutions that scale to millions of users. This role is designed for candidates graduating between December 2026 and July 2027. New Grad roles will be based in San Francisco or New York. Your background looks something like this Pursuing or completing a B.S. (or higher) in Computer Science or a related technical field, graduating between December 2026 and July 2027 Proficiency with Python, TypeScript, and asynchronous programming Track record of building and shipping high-quality products and features through coursework, internships, or personal projects Comfort digging into system failures across deep t