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Responsibilities:
- Build sandboxed environments that wrap real Niural workflows, stateful across episodes and seeded for reproducibility.
- Design programmatic verifiers from known ground truth, scoring trajectories to prevent reward hacking.
- Train agents via RL loops, curriculum schedules, and fine-tuning, then publish negative results internally.
Requirements:
- A published, peer-reviewed paper at a conference or journal, with clear personal contribution.
- Foundational knowledge of AI and ML, with strong Python and engineering skills (APIs, Docker, deployment).
- Experience with agent systems, reinforcement learning literacy, and reproducibility as a habit.
Why Join:
- You will build the training substrate for agents moving real money, with checkable ground truth.
- You will publish first-author research papers from novel work in global payroll and compliance.
- Your research sits next to the product, so you see it deployed rather than cited and forgotten.
Niural
Niural is a global Payroll, Employer of Record (EOR), Agent of Record (AOR), and Contractor Management platform that empowers businesses in the digital economy. We are a team building foundational internet infrastructure with a focus on speed, ownership, and ambition.