Applied Researcher - Sovereign AI

Prime Intellect is building the open superintelligence stack, the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Its platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. The role focuses on post-training and agent systems in public-sector and regulated-industry settings, where models and agents must operate within strict boundaries around data, deployment, access, auditability, and human authority.

Responsibilities

Turn a workflow into tasks, environments, evaluations, and reward signals; Run post-training experiments against real baselines and deploy what works; Determine whether gains come from the model or from data, harness, inference, or infrastructure; Build private improvement loops that turn sensitive traces into environments, training signal, evaluations, and deployable model updates within the operator's data boundary; Train tool-using domain agents with permissioned, traceable, recoverable actions evaluated against real procedures; Develop high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance; Work directly with customers, users, or research partners in externally facing roles; Run experiments on how privacy, access, and infrastructure constraints change learning

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; Desire 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 (meaningful baselines, careful trace inspection, clear failure attribution, willingness to change direction when evidence changes); Strong Python and ability to build artifacts other researchers and engineers can run, inspect, and extend; Comfort owning ambiguous problems and keeping partners and teammates current

Nice to have

Publications; open-source systems; benchmarks; shipped products as evidence of exceptional work; Experience showing something difficult built, what failed, and how evidence changed the approach

Benefits

Cash compensation range of $150-300k; Equity incentives; Flexible work (San Francisco, New York, London, or remote); Visa sponsorship and relocation support; Professional development budget; Team off-sites and conference attendance