Applied Researcher - Sovereign AI

The role

You will build post-training and agent systems in public-sector and regulated-industry settings. This involves creating improvement loops, agents with explicit permissions, and high-assurance evaluations.

What you will do

• Turn workflows into tasks, environments, evaluations, and reward signals
• Run post-training experiments against real baselines and deploy what works
• Work directly with customers, users, or research partners
• Build private improvement loops that turn sensitive traces into training signals and model updates
• Train tool-using domain agents with permissioned and traceable actions
• Develop high-assurance evaluations for reliability, calibration, robustness, and compliance
• Own improvement loops that run end to end inside the operator's boundary
• Own agents with explicit permissions, evidence trails, and human checkpoints
• Own evaluations that test reliability, calibration, recovery, and compliance
• Conduct experiments on how privacy, access, and infrastructure constraints change learning

Required qualifications

• Relevant experience in RL/post-training, private ML systems, high-assurance evaluation, agent safety, regulated-domain ML, public-sector technical work, or deployment under strict data and infrastruct
• Want to build and evaluate these systems directly
• Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops
• Strong experimental judgment
• Strong Python
• Ability to build artifacts that other researchers and engineers can run, inspect, and extend
• Comfort owning ambiguous problems

Nice to have

• Publications
• Open-source systems
• Benchmarks
• Shipped products

Benefits

• Cash Compensation Range of $150-300k + equity incentives
• Flexible Work
• Visa Sponsorship & relocation support
• Professional Development budget
• Team Off-sites & conference attendance

About the company

Prime Intellect is building the open superintelligence stack. The platform Lab unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system.