Production AI Engineer: LLM Systems & Datasets

A stealth frontier model lab is hiring a Post-Training Engineer. The company is pre-launch, sub-15 people, backed by top-tier investors, and heading toward a major general release in fall 2026. The founding team comes from OpenAI, Google Brain, and Meta FAIR. They're solving one of the hardest problems in AI: making LLMs reliable enough to build real autonomous workflows on. About the Role You will support the core training stack with a combination of data science, creativity, and engineering, building the datasets, evals, and interventions that determine how their models behave in production. What You'll Do Design and build reliable, production-grade AI systems integrating large language models and their proprietary models into real-world workflows Create high-leverage datasets, products, and user interfaces that unlock new capabilities on top of their model Run rigorous analyses and experiments to understand how data, training choices, and targeted interventions impact model behaviour and performance Develop and own evaluation frameworks to measure quality, reliability, and emergent characteristics across model iterations Partner with product managers and customers to translate real-world needs into concrete model and system improvements Continuously iterate and co-discover new techniques for building trustworthy, production-ready AI Who You Are 5+ years of professional software engineering or equivalent expertise Have shipped user-facing products end-to-end and can name what you built and who used it Strong API coding depth, the key differentiator for this role Product intuition: you think about how systems are used in the real world, not just technical correctness Hacker/builder DNA: you build things because you want to, not because you were asked to Comfortable with ambiguity; you take satisfaction in finding creative solutions Hands-on experience with LLMs and a clear understanding of their capabilities and limitations #J-18808-Ljbffr Hone