> Markdown version of [/jobs/ext/4011777-founding-ai-engineer](https://www.wearedevelopers.com/jobs/ext/4011777-founding-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding AI Engineer - **Company:** Recruiting from Scratch - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Experienced - **Salary:** $200,000.0 - **Contract:** Permanent contract - **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Software Debugging, Design of User Interfaces, Human-Computer Interaction, Python (Programming Language), Machine Learning, Regression Analysis, Node.Js, Open Source Technology, Performance Tuning, Azure Machine Learning, Systems Architecture, TypeScript, ReactJS, Large Language Models, Multi-Agent Systems, Reliability of Systems, Generative AI, Backend, Agentic-AI, Low Latency, Build Tools, Machine Learning Operations - **Published:** October 29, 2026 - **Apply:** https://www.careerbuilder.com/job-details/founding-ai-engineer-san-francisco-ca--34fc6c93-bff0-4c02-89f0-f5f3f7997d6b ## About the Role Artificial Intelligence (AI), Artificial Intelligence (AI), Funding, Architectural Services, Production Systems, Problem Solving Skills, System Architecture, Quality Metrics, User Interface/Experience (UI/UX), Data Modeling, 2+ years of hands-on AI engineering experience, with strong production LLM experience, * 2+ years of hands-on AI engineering experience. * Strong experience building production LLM systems. * Has shipped an AI/ML-powered system to real production users - not only a research prototype or internal demo. * Experience working at a company with a rigorous engineering bar. * Startup or growth-stage VC-backed experience strongly preferred. * Demonstrated ability to operate with high ownership and limited structure. * Experience taking AI systems from experimentation through production. * Strong candidates may come from AI engineering, ML engineering, applied AI, or highly technical software engineering backgrounds with significant AI ownership. Technical Requirements * Strong hands-on Python experience. * Production experience building AI/ML services. * Strong understanding of LLM-based systems. * Experience with prompting, retrieval, tool use, and agent orchestration. * Experience designing and building AI evaluation systems. * Understanding of model quality, regressions, latency, and cost. * Experience working with production APIs and backend systems. * Familiarity with TypeScript, Node.js, and React is valuable given the broader product stack. AI & Systems Requirements * Strong applied AI focus rather than purely theoretical or research-oriented ML. * Experience building LLM applications used by real customers. * Experience building evaluation frameworks that identify model regressions. * Experience with AI agents, tool calling, retrieval, or multi-step workflows. * ML or fine-tuning experience is a strong plus. * Ability to reason about whether a problem should be solved through models, data, infrastructure, or product design. * Strong production engineering fundamentals around reliability, scalability, latency, and cost. * Open-source projects, technical papers, or sophisticated side projects are strong additional signals. Soft Skills * Extremely high ownership and initiative. * Comfortable working in an early-stage startup environment. * Strong ability to operate with ambiguity. * Product-minded and commercially aware. * Comfortable making architectural decisions with incomplete information. * Fast learner who can adapt as AI tooling and techniques evolve. * Willing to be deeply hands-on rather than operating only at a strategic level. * Strong communication and technical judgment. * Comfortable working in a demanding, highly in-person environment. * Willing to take responsibility for systems after they reach production. ## Description This is a high-ownership, highly hands-on environment. You will work directly on production systems, ship frequently, and be responsible for understanding whether AI features actually deliver value for real users., * Own the AI layer across prompting, retrieval, tool use, agent orchestration, and model behavior. * Build production LLM systems that serve real customers. * Design and improve AI pipelines from experimentation through production. * Determine when to use prompting, retrieval, fine-tuning, or other approaches to solve product problems. * Take ownership of AI systems from architecture through deployment and iteration. 2. Build Evaluation & Reliability Infrastructure * Build evaluation harnesses that measure model and system quality before shipping changes. * Create evaluation systems capable of catching regressions in production AI behavior. * Establish metrics for model quality, reliability, latency, and cost. * Continuously monitor AI systems and use real-world behavior to improve them. * Develop practical evaluation frameworks rather than relying solely on qualitative testing. 3. Solve Ambiguous Product & AI Problems * Take ambiguous product problems and determine whether the underlying issue is related to models, data, infrastructure, or user experience. * Translate product requirements into practical AI system designs. * Experiment quickly and validate solutions against real-world outcomes. * Balance model quality with latency, cost, reliability, and merchant-scale requirements. * Work across product and engineering to turn new AI capabilities into customer-facing features. 4. Ship & Scale Production AI * Ship AI features to production on a frequent cadence. * Build systems capable of operating reliably at increasing scale. * Optimize inference, latency, and infrastructure costs. * Debug model-shaped production issues and improve system reliability. * Establish technical foundations that can support future AI engineers and product development.