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We're building the operating system for the next generation of computing — one where AI agents replace apps and your technology finally works for you instead of the other way around. We're a stealth-mode startup with a world-class founding team with deep roots in consumer AI, extended reality, and wearable technology — including founders of some of the most recognizable hardware and software platforms of the last decade. We're backed by strategic partnerships with leading silicon and manufacturing companies, and we're hiring our first AI engineer to build the intelligence layer at the core of the platform. This is a rare opportunity to architect the agent infrastructure of a platform that doesn't exist yet — at the layer where always-on contextual AI meets a wearable form factor for the first time. Additional product details shared under NDA. What We Offer Salary: competitive depending on experience Meaningful early-stage equity Full medical, dental, and vision coverage Fully remote with occasional in-person time in Silicon Valley or Paris for key milestones Awear is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Key Responsibilities The backend engineers build the infrastructure. The mobile engineers build the user facing surfaces. You build what runs between them — the agents themselves. As our first AI Engineer you will own the design and implementation of our agent layer — the pipelines, reasoning chains, memory retrieval systems, tool integrations, and orchestration logic that turn raw LLM capability into a platform that genuinely replaces the app paradigm. You will work directly with the CEO and across the full engineering team to make sure the agent experience is as technically rigorous as it is experientially compelling. This is a hands-on engineering role. You will write production code, own the agentic runtime architecture, and be directly accountable for the quality of every agent interaction on the platform. You will also be a key voice in decisions about which models to use, how to route between them, and how to structure the memory and context systems that make our platform smarter over time. We actively use AI development tools across our engineering team — Cursor, Claude, Copilot — and expect engineers who use them seriously as a core part of their workflow. What You'll Build The platform agent runtime — the core orchestration layer that manages agent sessions, chains reasoning steps, routes to tools, and executes actions on behalf of users Multi-provider LLM integration and routing — selecting and switching between regional and task-specific language models dynamically, with latency, cost, and capability all factored into routing decisions RAG architecture and memory retrieval — the systems that give agents access to the user's persistent, encrypted context layer and make responses smarter and more relevant over time Tool and skill integration pipelines — the infrastructure that connects agents to external APIs, device capabilities, and first-party platform features Agent evaluation and observability — the frameworks that measure agent quality, surface failures, and give the team visibility into how agents are actually performing in production On-device inference optimization — working with the mobile and firmware teams to identify which parts of the agent pipeline can run locally on device, reducing latency and cloud dependency as the platform evolves toward wearable hardware Prompt architecture and system design — the structured prompting frameworks, system instructions, and context management patterns that govern agent behavior consistently across the platform Ideal Experience 4+ years of software engineering experience with at least 2 years working directly on LLM-based systems in production Deep hands-on experience with LLM integration — not just API calls but genuine understanding of how to build reliable, scalable, low-latency AI pipelines Strong experience with RAG architectures — vector databases, embedding pipelines, retrieval optimization, context window management Familiarity with agent frameworks and orchestration patterns — LangChain, LlamaIndex, AutoGen, or similar, with a clear point of view on their strengths and limitations Solid Python engineering skills — you are writing production code, not research notebooks Experience evaluating LLM outputs at scale — building evals, measuring quality, detecting regressions Genuine understanding of the tradeoffs between different LLMs — capability, latency, cost, privacy implications — and experience making routing decisions in production systems Active user of AI-assisted development tools with a genuine point of view on how to use them well Strong written English and proven ability to work effectively in a remote and distributed team Strong plus: experience with on-device or edge inference — Core ML, ONNX, TensorFlow Lite, or similar; multi-agent system design; always-on or streaming inference architectures; privacy-preserving AI systems; wearable or mobile AI platform experience
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.