AI Engineer

Normal Computing CorporationNew York City, NY

About The Position

We are looking for an AI Engineer to 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.

Requirements

  • 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.
  • Understanding of AI safety, bias mitigation, and ethical considerations.
  • Ability to explain complex AI concepts to both technical and non-technical stakeholders.

Nice To Haves

  • 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.

Responsibilities

  • Lead end-to-end 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 our platform.
  • Guide junior engineers and establish best practices for AI development
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