AI Engineer

Stack

Responsibilities

• ▹Lead AI development from initial concept through production deployment and iteration
• ▹Design and implement LLM-powered solutions that extract meaning from complex technical specifications
• ▹Handle multi-modal complexity and explore multi-agent and RL approaches for agentic code generation and tool use
• ▹Design strategies to manage latency, output variance, and graceful error handling at scale
• ▹Collaborate with product and engineering teams to embed AI capabilities seamlessly into the platform
• ▹Guide junior engineers and establish best practices for AI development

Requirements

• ▹Previous experience delivering production AI systems involving language models, preferably 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

Nice to have

• ▹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

Soft skills

About the company

Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource — stochastic, in-memory, asynchronous architectures that deliver 10-100x more AI inference per dollar and per watt. The company co-designs the full stack, from AI-native EDA systems used by the world's largest semiconductor companies to the advanced ASICs they make possible. Backed by $85M+ from leading deep-tech investors, the team works across New York, Silicon Valley, London, Copenhagen, and Seoul.