AI Research Engineer

Normal Computing Corporation • New York City, NY

4h

About The Position

The Normal Team builds foundational software and hardware that help move technology forward - supporting the semiconductor industry, critical AI infrastructure, and the broader systems that power our world. We work as one team across New York, San Francisco, Copenhagen, Seoul, and London. Your Role in Our Mission: We’re hiring an AI Researcher / AI Research Engineer to push the frontier of agentic LLMs and reinforcement learning for our agentic code generation tool, nectar. 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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• Design and implement multi‑agent and RL approaches for agentic code generation and tool‑use.
• Build research prototypes that integrate with nectar; 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 This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree