Contextual Design, Inc. - Founding AI Engineer
Requirements
• Deep LLM expertise: You understand model inference, fine-tuning, context optimization, and prompt engineering at a production level—not just API calls • ML infrastructure: Experience with MLOps/LLMOps practices, model training pipelines, and deployment at scale • Core skills: Python, experience with neural architectures (transformers, RNNs, CNNs), data pipelines (ETL) • Cloud platforms: Production experience with AWS/GCP/Azure • Bonus: Reinforcement learning experience—we're building systems that learn from user choices • Christian (CEO) built design systems for 25M users and partnered with YC companies on product design. Emanuel (CTO) was a Principal Architect at AWS, leading high-stakes 0-1 product builds. We're teaming up at the exact moment code and design are converging—to build the AI product designer. We're looking for founding-caliber engineers ready to shape what’s next.
Responsibilities
• In one sentence: Build the agentic reasoning system that turns design expertise into production AI. • Own our multi-agent orchestration layer—how specialized AI systems collaborate to think through design problems • Design RAG pipelines that retrieve and apply design patterns at the right moments • Build evaluation frameworks that measure "senior designer quality" at scale • Experiment with frontier approaches—we need breakthrough results, not incremental gains • Shape technical strategy—you're defining what's possible, not just executing • Success in 6 months: Our output consistently matches or exceeds what mid-level designers produce—in seconds instead of days. • You're a Great Fit If: • You've solved the hard problems, not just used the frameworks: • You've designed RAG systems from scratch—made real architectural decisions about chunking strategies, embedding models, retrieval approaches, and re-ranking. You know why naive similarity search fails and what actually works. • You've debugged agentic systems in production—dealt with loops, hallucinations, tool selection failures, context window management. You know where multi-step reasoning breaks and how to make it reliable. • You've built your own evaluation frameworks—not just vibes-based testing. You've measured quality systematically because you needed to know if changes actually improved things. • You can explain what went wrong in past projects—you've hit the wall where "just add more context" stops working, where prompts become unmaintainable, where latency kills UX • Bonus points: Experience with reinforcement learning—we're building systems that learn from user choices and improve over time
Benefits
• Anyone can generate random UI. We're solving something no one else has: • Context-aware design intelligence - Understanding a product's existing design system, component patterns, layout structure, and brand conventions, then generating new designs that feel native, not generic. • Codifying design excellence - The principles, methods, and approaches used by world-class product designers aren't written down anywhere. Your job is to encode human design expertise—how elite designers actually think, what makes design feel right, why certain approaches work—into AI systems. • Consistent quality at scale - Anyone can get one good output. We need every output to be good enough to ship. That means understanding context (design systems, product patterns, brand language), applying sophisticated reasoning, and hitting production quality every single time. • Speed without compromise - Designers take days because quality demands it. We need to compress that thinking into seconds without losing what makes it good. • The bar: Designs so good that PMs trust them over waiting for their designer. Not "good for AI"—actually good. Your job: Shape agentic capabilities that consistently produce designs worth shipping. • This isn't another RAG chatbot or generic AI tool. You're solving a problem that no current AI can solve—and if we're right that by 2027 every product team needs an AI designer, you're building category-defining infrastructure from the ground floor. • Small team means your decisions shape everything. The constraint (must reach senior designer quality) forces genuine innovation.
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