Applied Research - Sovereign AI

Prime Intellect•San Francisco, CA

5d•$150,000 - $300,000

About The Position

Prime Intellect is hiring an Applied Researcher to build post-training and agent systems for national institutions, public-sector organizations, and regulated industries. The work focuses on enabling capable models and agents to operate within strict requirements for data, deployment, access, auditability, and human authority, while allowing organizations to improve systems using their own workflows. Researchers turn workflows into tasks, environments, evaluations, and reward signals; run post-training experiments against real baselines; and deploy effective results. They investigate whether measured improvements come from the model, data, harness, inference, or infrastructure. Reusable work may become shared research, open-source systems, and product capabilities. The role is externally facing, with most researchers working directly with customers, users, or research partners while maintaining a substantive research agenda. Work takes place within partner constraints, sometimes on partner infrastructure and under partner access rules.

Requirements

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 infrastructure constraints.
Willingness 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, including meaningful baselines, careful trace inspection, clear failure attribution, and willingness to change direction when evidence changes.
Strong Python skills and ability to build artifacts that other researchers and engineers can run, inspect, and extend.
Comfort owning ambiguous problems and keeping partners and teammates current while resolving them.

Nice To Haves

Evidence of exceptional work, such as publications, open-source systems, benchmarks, or shipped products.
Experience working on public-sector or regulated-industry systems, private ML, high-assurance evaluations, agent safety, or deployment under strict data and infrastructure constraints.

Responsibilities

Build private improvement loops that transform sensitive traces into environments, training signals, evaluations, and deployable model updates without crossing an operator’s data boundary.
Train tool-using domain agents with permissioned, traceable, and recoverable actions, evaluated against real procedures.
Develop high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance under adversarial or changing conditions.
Own end-to-end improvement loops within an operator’s boundary, from trace selection through deployed updates.
Build agents with explicit permissions, evidence trails, bounded tools, and human checkpoints.
Create evaluations that test reliability, calibration, recovery, and compliance with real procedures.
Experiment on how privacy, access, and infrastructure constraints affect learning, and develop methods that transfer across high-assurance settings.
Work directly with customers, users, or research partners while maintaining a substantive research agenda.
Run post-training experiments against real baselines, investigate results, and deploy what works.
Communicate with partners and teammates while resolving ambiguous problems.