AI Engineer
Normal Computing
About Normal Computing Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt. We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing. Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority. 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'll 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 capabilities seamlessly into our platform.
- Mentorship: Guide junior engineers and establish best practices for AI development.
- 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 concepts to both technical and non-technical stakeholders
- 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
Vacancy posted 3 days ago
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