AI Research Engineer
Summary
Research and build agentic LLMs and reinforcement-learning systems for code generation, running experiments, curating datasets, and shipping production-quality research code.
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.
Your Role in Our Mission
We’re hiring an AI Research Engineer to push the frontier of agentic LLMs and reinforcement learning for our agentic code generation tool. You’ll design and run experiments, build agents, curate datasets from complex technical documents (e.g., chip specifications), and create rigorous evaluations. You’ll write production‑quality research code and work closely with engineering to ship improvements to customers. Leadership not required—impact through research and building is.
Responsibilities
Design and implement multi‑agent and RL approaches for agentic code generation and tool‑use.
Build research prototypes that integrate with our agentic code generation tool; collaborate to productionize wins.
Create evaluation suites: task specs, pass/fail checkers, coverage, cost/latency dashboards.
Acquire and curate datasets from PDFs/logs/tables; generate synthetic data where appropriate; maintain data cards and licensing.
Analyze experiments with disciplined ablations; document results and decisions.
Stay current on LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, and program synthesis.
What Makes You a Great Fit
PhD in CS/AI/ML (or equivalent research experience) with publications ideally in multi‑agent RL, agentic AI, or RL for language/code.
Strong Python and ML framework experience (PyTorch preferred; JAX/HF a plus).
Demonstrated ability to turn research into working systems; reproducibility mindset (tests, seeds, configs, logging).
Experience designing eval harnesses and success metrics for sequential/agentic tasks.
Comfortable with data acquisition/curation from documents/logs; good instincts about data quality and licenses.
Clear communicator who partners well with engineers.
Bonus Points For
Research on program synthesis/codegen, constrained decoding, or execution‑based rewards.
Experience with offline RL from tool traces or human corrections.
Open‑source contributions (e.g., CleanRL, RLlib, AutoGen, LangGraph, CrewAI, Transformers).
Familiarity with semiconductor/chip domains or other complex technical specs.
Track record of shipping research to production and measuring impact.
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, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
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