AI Research Resident

About the company

Normal Computing builds AI-driven EDA software and physics-based computing hardware for semiconductor design.

About this role

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, San Francisco, London, Copenhagen, and Pangyo. THE RESIDENCY PROGRAM The AI Research Residency is Normal Computing's flagship program for exceptional researchers and engineers who want to work at the frontier of agentic AI. Residents join a small, hand-picked cohort with a dedicated research mentor, direct access to the team building our agentic code generation platform, and a clear arc from onboarding through publication. Every residency is built around two milestones that mark you as part of something bigger than a single project: a research paper co-authored with our team, and a company-wide research colloquium where you present your findings to the full Normal Computing organization. You'll leave with a body of published, presented work, and a standing as one of the earliest residents to help define what this program becomes. YOUR NORMAL EXPERIENCE - You'll spend your residency embedded with the team advancing agentic LLMs and reinforcement learning at Normal Computing — designing experiments, building agents, and creating the evaluations that tell us whether any of it actually works. - This is a hands-on research residency: you'll take ownership of a real technical problem in agentic code generation and tool use, work alongside the researchers and engineers building our platform, and be expected to contribute ideas, not just execute someone else's. - Your job is to help turn research into production-quality research code that a team can build on, and, where the work is ready, into customer-facing improvements. Cross-functional collaboration with our hardware team will be encouraged, for example by bringing novel AI tools to enable our hardware efforts via recursive self improvement. Outside-of-the-box thinking is encouraged. - You'll get direct exposure to the pace, ambiguity, and speed of decision-making that comes with working at a fast-moving, well-funded startup — where the distance between an idea on a whiteboard and a feature in front of a customer is measured in weeks, not years. WHAT YOU'LL DO - Develop multi-agent and RL strategies. Help build multi-agent and reinforcement learning strategies for agentic code generation and tool use, and turn them into research prototypes integrated with our code generation platform. - Build evaluation suites and dashboards. Help build comprehensive evaluation suites — task specifications, benchmarks, and performance dashboards — that tell us honestly how agentic and sequential-decision systems are actually performing. - Acquire and curate datasets. Source and curate datasets from technical documents and other materials, and generate synthetic data where real data is scarce or the task calls for it. - Drive a research question of your own. Partner with researchers and engineers to scope, run, and iterate on an original technical investigation, with rigorous experimental analysis and documentation along the way. - Co-author a paper. Work with the team to write up your findings for submission to a relevant venue, with mentorship on framing, experiments, and technical writing along the way — one of the two milestones every resident builds toward. - Present at the residency colloquium. Share your work and thinking with the broader Normal Computing research community at the program's capstone event, and get real-time feedback from people building this technology every day. - Experience startup pace firsthand. Work directly with founders, senior researchers, and engineers in a lean, fast-moving environment where priorities shift quickly and your work has an outsized, visible impact. WHAT WOULD MAKE YOU A GREAT FIT - Currently pursuing or recently completed a graduate degree (MS or PhD, or equivalent research experience) in computer science, AI, machine learning, or a related field. Publications are a strong plus, but not required at the resident level — a solid research track record is what matters. - Strong Python skills and proficiency with modern ML frameworks (PyTorch preferred). - Familiarity with agentic LLM concepts — multi-agent systems, tool use, reinforcement learning variants, constrained decoding, program synthesis — and a genuine interest in keeping current with the field. Deep production experience isn't expected at the resident level, but the intuition should feel familiar. - Some experience (course projects, research, or otherwise) turning a research idea into working, reasonably reproducible code — you care about whether results replicate, not just whether a demo works once. - Comfort with, or eagerness to learn, the practical side of research: acquiring and curating datasets, thinking through licensing and provenance, and building evaluation frameworks for sequential or agentic tasks. - Bonus: prior work in program synthesis, code generation, constrained decoding, or offline RL; open-source contributions to frameworks like CleanRL, LangGraph, or Transformers; or exposure to the semiconductor domain. - Strong written and verbal communication skills; prior experience writing up research (papers, theses, technical reports) is a plus, as is any experience presenting technical work to a live audience. - A bias toward ownership and self-direction — you're energized, not overwhelmed, by the ambiguity and speed of a small, fast-moving startup. Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, r