AI Engineer

Normal Computing | Build with Us Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul. The Role As an AI Engineer at Normal, you will build production systems that understand large technical documents, like chip design specifications, and turn them into code. You'll ship real improvements to customers weekly while pushing the boundaries of what's possible in AI for hardware through reinforcement learning, agentic coding, and exceptional software engineering. What You Will Own End-to-End Development: Lead AI development from initial concept through production deployment and iteration. Document Understanding: Design and implement LLM-powered solutions that extract meaning from complex technical specifications. Agentic Systems: Handle multi-modal complexity and explore multi-agent and RL approaches for agentic code generation and tool use. Production Quality: Design strategies to manage latency, output variance, and graceful error handling at scale. Platform Integration: Collaborate with product and engineering teams to embed AI capabili [... source excerpt omitted ...] ties seamlessly into our platform. Mentorship: Guide junior engineers and establish best practices for AI development. What Makes You a Great Fit Previous experience delivering production AI systems involving language models, preferably involving document understanding and/or agentic workflows Solid software engineering skills with experience in distributed systems and production-grade code Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, transformers) Hands-on experience with prompt engineering, fine-tuning, and deploying large language models Ability to wrangle, clean, and preprocess large-scale, heterogeneous datasets Ability to explain complex AI c [... source excerpt omitted ...] on-technical stakeholders Bonus Points Experience with hardware design, hardware verification, or EDA tooling Experience deploying AI systems in mission-critical or high-stakes production environments Experience with cloud platforms (AWS, GCP, Azure) for large-scale AI infrastructure Research or applied experience with LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, or program synthesis Open-source contributions or publications in AI/ML venues Skill in balancing cutting-edge innovation with production reliability and pragmatism Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committ