Senior Product Engineer

Who we are Muse AI builds Generative Enterprise Agent (GEA) systems for global brands. We’re hiring a Product Engineer who can turn real customer workflows into useful, trustworthy software — not just a convincing demo. Beyond the minimum experience requirement, we care more about your ability to learn quickly, communicate clearly with customers in English, and ship high-quality products with ownership than your title. About ingenOPSYou’ll work on ingenOPS, Muse AI’s collaborative creative-agent system. GEA supplies consumer insight, brand assets, product information, business context, and operating safeguards; ingenOps turns that context into editable creative work—from ideation and refinement to multi-channel, multi-size, multi-language, and multi-market production. People remain in control at key decisions, and each campaign’s approved learning can become reusable brand Skill or Memory. Coding agents (Claude Code, Codex, or similar) are table stakes here. You already use them to ship, and you own what merges. What we care about most Understand the customer — listen, dig into workflows, constraints, stakeholders, and success criteria.Synthesize — turn messy asks into a clear problem, scope, and technical approach.Ship a trusted product — build, evaluate, deploy, and improve it until it holds up in real usage.Everything else supports that loop. What you’ll do Work directly with customers and the internal team to understand workflows and identify valuable problems to solve.Turn discovery into prototypes that prove value and clarify a path to production.Design, build, test, and deploy AI-powered product workflows across the stack.Build AI capabilities such as tool calling, structured output, streaming, context and knowledge retrieval, guardrails, and human-review flows.Define success criteria and representative evaluations; instrument and improve quality, reliability, latency, cost, and user outcomes.Design clear experiences for agent progress, uncertainty, errors, review, and escalation — not just model output.Turn repeated customer needs into reusable product capabilities, APIs, tools, and documentation.Work with the team to make product and technical decisions, contribute to shared platform capabilities, and grow into broader ownership over time. Tech stack Muse AI’s product stack includes: Svelte / SvelteKit, TypeScript, and modern web developmentPostgreSQL, APIs, webhooks, and authenticationGit / GitHub, CI/CD, and production engineering practicesAI systems including tool calling, structured outputs, streaming, context and knowledge retrieval, guardrails, evaluations, and observabilityOur wider group also works with React. You do not need prior Svelte experience if you are a strong product engineer who can learn a modern frontend stack quickly. AI-native development (required) You already use coding agents as a normal part of how you ship software. You can: Break ambiguous work into tasks an agent can execute effectively.Give agents strong context, specifications, and acceptance criteria.Use agents to navigate codebases, build, test, debug, and refactor.Review AI output critically and own the quality of everything that ships.What you bring You have at least 5 years of engineering experience. You do not need a computer-science degree or founder experience. We care about evidence that you can: Take an unclear problem, ask the right questions, and turn it into a useful product.Build and ship working software across the stack.Communicate directly and clearly with customers in professional English: understand their workflow, explain tradeoffs, and follow up concisely in writing.Use AI coding tools effectively while owning quality, security, and correctness.Learn unfamiliar domains, stacks, and codebases quickly.Make sensible tradeoffs between speed, product quality, reliability, and scope.Work independently while asking for help early when risk or ambiguity is high.A strong portfolio can include professional work, open source, serious side projects, freelance or client work, internships, or something you built for yourself. Helpful, but not required Experience building customer-facing web products.Experience working with clients, users, or non-technical stakeholders in English.Experience with LLM-powered products: structured outputs, tool calling, agents, MCP, RAG, embeddings, context management, evaluations, or observability.Experience in enterprise AI, workflow automation, agent platforms, or complex B2B products.Familiarity with AWS, Vercel, Cloudflare, Supabase, Redis, Docker, or similar tools.Location and work authorization Taipei (Xinyi) · Hybrid · Full-time · Muse AI / 妙偲 You must be able to work from our Taipei office as part of a hybrid working arrangement and be legally authorized to work in Taiwan. How to apply Send your resume plus a GitHub profile, live product, or short note about something you personally shipped.