Full Stack Engineer

We are looking for a full-stack engineer with 2-5 years of experience who can make scalable architectural decisions and own the product layer around our AI core. You should be comfortable building real-time video pipelines, backend APIs, and mobile/desktop clients for industrial-facing wearable AI products., * Ship the end-to-end product experience: smart glasses to backend to companion app that real customers can use without an engineer present.

• Build and optimize the real-time video/streaming pipeline connecting the glasses to the AI core within tight latency budgets.
• Own the backend APIs and data layer the glasses and AI services depend on, keeping them reliable, observable, and scalable to early customer load.
• Build the mobile/desktop client for setup, monitoring, and workflow configuration.
• Stand up the deployment and release path so new builds reach hardware in the field safely.
• Define product architecture and engineering practices as the team scales, making smart, scalable decisions early that won't need reworking.
• Be the cross-stack generalist who unblocks the team: debug from frontend through backend to device.

Tech stack: TypeScript, Python, React, React Native, Node.js, AWS, GCP, Supabase, Railway, WebRTC, PostgreSQL, Redis, Docker, real-time streaming, REST APIs, CI/CD

• 2-5 years of post-grad software engineering experience, ideally with early-stage startup exposure.
• Track record of building and shipping scalable systems zero-to-one with real user traction, owning the whole app end-to-end (backend, APIs, data layer, and the frontend/mobile client a real user touches).
• Experience as a founding or early engineer, or at an early startup that shipped, or at a high-intensity engineering company.
• Full-stack ability on a modern startup stack (AWS, Supabase, Railway, GCP, and similar).
• Strong CS/engineering background, or a demonstrated equivalent shipping record.
• Makes mature, scalable architectural decisions independently.

Nice to Have

• Real-time video, streaming, or low-latency pipeline experience.
• Experience shipping AI pipelines into a product (LLM/agent integration).
• Frontend, design, or UX craft.
• Experience shipping mobile applications (iOS, Android, or strong React Native with native bridges).
• Signals of grit: olympiad medals, USACO Platinum, competitive programming rank, or having founded something while studying., * Intro Call (15-45 mins): A conversation covering your background, motivation, and a deeper dive into past work.
• Live Coding Interview (60 mins): A live coding session with the founders covering full-stack ability, system design intuition, and how you handle real-time, latency-sensitive scenarios.
• On-site: An in-person visit to meet the team, see the product, and discuss strategy, go-to-market, and day-to-day collaboration.

We are an early-stage, venture-backed startup building wearable AI co-pilots for industrial field workers. Our smart glasses are powered by agentic vision-language models (VLMs) and are being deployed into legacy industries such as data centers, energy, and aerospace, where AI has never meaningfully helped before. Rather than being another LLM wrapper, our product grounds computer vision and agentic VLMs on deep integrations with the enterprise systems these industries actually run on. We are already deployed with enterprise customers and have a strong pipeline of additional accounts. We run with the intensity of a top-tier startup and value hunger, scrappiness, and genuine mission alignment over pedigree. Engineers operate with high ownership, move fast, and iterate constantly as the AI landscape evolves. The work is collaborative, in-person, and high-tempo, with a strong emphasis on shipping production systems to real customers. Why Join * Real impact: Our AI-powered smart glasses are already deployed with enterprise customers in demanding industrial environments, with a growing pipeline of accounts. * A differentiated bet: Computer vision and agentic VLMs grounded on real integrations with the systems industrial customers depend on. * Architecture ownership from day one: Define how the product layer scales as customer fleets ramp into the thousands of technicians. * Technically meaty work: Real-time video pipelines, low-latency streaming, and mobile plus desktop plus backend built for wearables. Video on wearables is one of the harder versions of full-stack right now.