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About FuriosaAI FuriosaAI builds high-performance, high-efficiency AI compute for the Inference Era. Founded in 2017 by veteran semiconductor and AI algorithm engineers, Furiosa operates globally with offices in Korea and Silicon Valley, along with a compiler-focused R&D lab in Lisbon. Our vision is to make AI computing sustainable, enabling access to powerful AI for everyone on Earth. We solve the AI hardware energy and operational cost crisis at the architectural level, rather than through brute force, building the world's first truly AI-native compute platform to unlock the full potential of artificial intelligence for every enterprise. About the Role Owns the developer-facing documentation that makes FuriosaAI's software stack — the Furiosa SDK and Furiosa-LLM — usable, from API references and conceptual guides to tutorials, quickstarts, and release notes. This is a docs-as-code role: reading source, running the stack on real hardware, and producing precise, verifiable documentation that keeps pace with a fast-moving compiler, runtime, and inference codebase — backed by automated pipelines that continuously validate that documentation against the live codebase and hardware. Key Responsibilities Owns and writes developer documentation for the Furiosa SDK and Furiosa-LLM — API references, conceptual guides, tutorials, quickstarts, and migration guides — translating complex systems behavior into clear, accurate, task-oriented content. Builds, maintains, and operates the documentation toolchain and docs-as-code pipeline (MDX-based static sites, automated API-reference generation, automated link and sample validation in CI), treating documentation as a versioned, testable artifact that is continuously verified against each SDK release. Designs and runs automated, hardware-in-the-loop validation pipelines that continuously authors and verifies runnable code samples and end-to-end examples against real RNGD hardware and released packages, catching drift early and ensuring every snippet compiles, runs, and reflects current APIs. Partners with engineers across diverse teams to capture design intent and surface accurate technical detail, then drives documentation to keep up with release cycles, deprecations, and breaking changes. Defines and enforces documentation standards — style, terminology, information architecture, and versioning — so content stays consistent and discoverable across the SDK and Furiosa-LLM. Produces release notes, changelogs, and upgrade and migration guidance that clearly communicate what changed across SDK and Furiosa-LLM versions. Minimum Qualifications Bachelor's degree in Computer Science or equivalent work experience, with 3+ years writing technical documentation for developer-facing software (SDKs, APIs, systems software, or ML frameworks). Strong written English with the ability to explain low-level, systems-level concepts precisely and concisely. Working proficiency in Python and command-line tooling; comfortable reading source code, running build and inference workflows, and writing and validating code samples. Fluency with Git-based docs-as-code workflows and Markdown/MDX, including running documentation checks in CI. Solid understanding of deep neural networks (DNNs) and large language models (LLMs) — how they are built, run, and served — sufficient to document inference workflows accurately. Strong communication skills for cross-team requirement gathering and technical alignment. Preferred Qualifications Experience documenting ML inference frameworks, compilers, runtimes, or accelerator/GPU software stacks. Familiarity with LLM inference concepts (serving, batching, quantization, KV cache, distributed inference) and the PyTorch / Hugging Face ecosystem. Familiarity with GPU Kernel programmings (e.g., CUDA, Triton) Experience building and operating documentation platforms (e.g., MDX-based static site generators, Fumadocs, Mintlify, Sphinx) and automated API-reference pipelines. Experience designing information architecture and versioned documentation for software with frequent releases. Fluency in Python and Rust programming, sufficient to read, write, and validate code samples across the stack. Contact recruit@furiosa.ai
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.