Member of Technical Staff (Offensive Security Engineer)

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

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search systems, datasets, and models work together to create the best possible experience for our users. As our product and agent capabilities evolve, we need evaluation systems that are fast, reliable, production-faithful, and actionable. In this role, you will build and improve the technical foundations that support Answer Quality across Perplexity. This includes our shared evaluation infrastructure and the platform used to replay and analyze agent traces. You will work closely with data scientists, engineers, and product teams to identify quality problems, measure their impact, and turn evaluation findings into product improvements. Responsibilities Build shared evaluation infrastructure that helps teams run reliable evals, analyze results, and make product and model decisions Develop the platform for replaying and analyzing agent traces to reproduce production behavior and diagnose failures Build and operate scalable systems for processing, storing, and monitoring interaction, trace, and evaluation data Partner with data scientists, engineers, and product teams to turn answer-quality problems into evaluations, analyses, and product improvements Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality Qualifications 4+ years of software, data, or machine learning engineering experience shipping and operating production systems Strong proficiency in Python and SQL, with solid fundamentals in system design, data modeling, and distributed systems Experience building big-data systems, including distributed compute, large-scale storage, and high-volume pipelines Demonstrated ownership of ambiguous technical projects from initial design through production operation Ability to work effectively with

About the Role The Enterprise Knowledge Platform team builds the data layer that lets Perplexity's agents reach into the world's software. This team owns the systems that turn hundreds of heterogeneous integrations (native, MCP, CLI, first-party, and third-party APIs) into one unified, reliable, well-typed surface that agents can call with confidence. This platform is the core layer that forms the knowledge layer for Computer: it is how the agent discovers what tools exist, understands what each one means, decides which to call, and grounds its reasoning in real, permissioned, up-to-date enterprise data. We maintain a knowledge layer above connectors that pushes and pulls context into them, rather than letting each connector hoard org knowledge on its own, making Computer the source of truth for institutional knowledge. Models are commoditizing; grounded, actionable, permissioned access to a customer's real systems is not. When this layer is fast, accurate, and semantically rich, every agent built on top of it gets smarter; when it is weak, no amount of model quality compensates. Key Responsibilities Own the design and implementation of the connector runtime, the system that registers, hosts, and executes built-in connectors, hosted MCP servers, and CLI-backed tools behind a single agent-facing interface. Build and extend the semantic layer: tool and entity schemas, capability metadata, relationship modeling, and the mechanisms for capturing and applying organization- and account-specific corrections and knowledge. Design the tool-discovery and tool-selection surfaces that agents use to find the right connector and call it correctly, optimizing for both model accuracy and context efficiency. Make agent loops robust: structured results, partial-failure and retry semantics, idempotency, pagination, rate-limit handling, and observability into every tool call an agent makes. Define authentication, authorization, and credential-isolation patterns for connectors (OAuth fl

Perplexity is looking for experienced Generalist Software Engineers to join our Perplexity and Comet application teams. Our team is reshaping knowledge work through our high-impact desktop and Comet apps. The ideal candidate should have strong programming skills and multiple years of experience shipping high-quality, production software. Our stack spans several different skillsets, including Rust, Kotlin, Swift, Typescript, Chromium and Perplexity’s core AI stack. We have found that our strongest engineers are capable of working across multiple codebases and ramping up quickly, and we expect the same high bar for candidates. Responsibilities Work closely with product teams to implement novel mobile experiences. Work closely with design teams to design fast and intuitive UI. Utilize Perplexity Computer and other Agentic coding tools to accelerate development. Collaborate with data science and machine learning teams to instrument, analyze, and improve the end-to-end experience. Work with infrastructure and QA teams on deployment processes, including testing, release, and monitoring. Requirements 4+ years industry experience Experience in shipping high-quality native applications Architecture experience in writing code understandable by both agents and humans Deep understanding of app performance Ability to own projects and surfaces, and to push them forward proactively

About the Role We're hiring software engineers for the Model Behavior team to help shape how Perplexity’s AI products behave: the style of their responses, and the way they use tools, skills, and memory. The team designs prompt and context engineering strategies to deliver high-quality user experiences across multiple domains and models. The ideal candidate for this role has a strong software engineering background, and an analytical, experiment-driven approach to solving challenging problems. You’ll work on context and prompt engineering to shape model behavior and style, and to guide how models use tools, skills, and memory across our products. What you'll do Context Engineering: Design, test, and optimize the prompts, skills, tools, and memory that shape Perplexity responses across products, features, and use cases. Build self-improvement loops to steer the prompt, improve tool/skill use, and improve the ability to draw on memory. Model Releases: Help experiment with and release new models. Research & Analysis: Identify inconsistencies and failure modes in model outputs through well-designed research projects, for both internal and production systems. Knowledge Sharing: Help engineers across teams build intuition for prompt design and context engineering best practices. Staying Current: Track the latest prompting, context engineering, and alignment techniques from industry and academia, and bring the best ideas back to the team. What We're Looking For Required 2 to 10+ years of experience in software engineering or research. Strong background in software engineering fundamentals, and a technical understanding of LLM-driven and agentic systems. Experience shaping LLM behavior through prompts, tool and skill design, or memory systems. Strong written and verbal communication skills, particularly in explaining complex concepts to diverse stakeholders. Nice to have Recent experience working on modern LLM-driven products. Experience working across teams or with ext