[Hiring] Applied Research - Sovereign AI @Prime Intellect

Role Description

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale.

National institutions, public-sector organizations, and regulated industries need capable models and agents that operate within strict boundaries around data, deployment, access, auditability, and human authority. You will build post-training and agent systems in public-sector and regulated-industry settings.

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 the data, harness, inference, or infrastructure.
• Develop reusable parts that become shared research, open-source systems, and product capabilities.
• Work directly with customers, users, or research partners while maintaining a substantive research agenda.

Role Scope

• The work happens inside partner constraints, sometimes on their infrastructure and under their access rules.

Representative Early Problems

• Build a private improvement loop that turns sensitive traces into environments, training signal, evaluations, and deployable model updates without crossing the operator's data boundary.
• Train a tool-using domain agent whose actions are permissioned, traceable, recoverable, and evaluated against real procedures rather than generic benchmarks.
• Develop high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance with domain constraints under adversarial or changing conditions.

What You Will Own

• Improvement loops that run end to end inside the operator's boundary, from trace selection to deployed update.
• Agents with explicit permissions, evidence trails, bounded tools, and human checkpoints.
• Evaluations that test reliability, calibration, recovery, and compliance with real procedures.
• Experiments on how privacy, access, and infrastructure constraints change learning, with methods that transfer across high-assurance settings.

Qualifications

• Relevant experience: 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.
• 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, and a willingness to change direction when the evidence changes.
• Strong Python and the ability to build artifacts that other researchers and engineers can run, inspect, and extend.
• Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them.
• Evidence of exceptional work: publications, open-source systems, benchmarks, and shipped products.

Benefits

• Cash Compensation Range of $150-300k + equity incentives
• Flexible Work (San Francisco, New York, London, or remote)
• Visa Sponsorship & relocation support
• Professional Development budget
• Team Off-sites & conference attendance