Applied Research – Sovereign AI

Prime Intellect

. Build post-training and agent systems in public-sector and regulated-industry settings.

Posted 10/19/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $150,000 - $300,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and evaluating private machine learning systems within regulated environments, focusing on agent safety, compliance, and high-assurance evaluations. Proven ability to conduct research on privacy and infrastructure constraints while collaborating with stakeholders to resolve complex problems.

Highest-signal resume keywords

Reinforcement Learning/Post-TrainingPrivate Machine Learning SystemsHigh-Assurance EvaluationAgent SafetyRegulated-Domain Machine Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonModel-TrainingPost-TrainingAgent DevelopmentExperimental LoopsEvaluation TechniquesData Trace InspectionFailure AttributionArtifact BuildingDebugging

Soft Skills

Experimental JudgmentProblem OwnershipCommunication

Industry Keywords

Public-SectorRegulated IndustryCompliancePrivacyInfrastructure Constraints

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build post-training and agent systems in public-sector and regulated-industry settings.
Turn workflows into tasks, environments, evaluations, and reward signals.
Run post-training experiments against real baselines and deploy successful results.
Determine whether gains come from the model, data, harness, inference, or infrastructure.
Build private improvement loops within sensitive-data boundaries, from trace selection through deployed updates.
Train permissioned, traceable, recoverable tool-using domain agents evaluated against real procedures.
Develop high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance.
Own agents with explicit permissions, evidence trails, bounded tools, and human checkpoints.
Conduct research on how privacy, access, and infrastructure constraints affect learning.
Work directly with customers, users, or research partners while maintaining a substantive research agenda.

Requirements

What you’ll need

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.
Publications, open-source systems, benchmarks, and shipped products are valued as evidence of exceptional work.

Benefits

Comp & perks

Equity incentives
Flexible Work (San Francisco, New York, London, or remote)
Visa Sponsorship & relocation support
Professional Development budget
Team Off-sites & conference attendance