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Applied Research - Sovereign AI

Prime Intellect· Applied Research
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San Francisco, New York, Remote, London$150k - $300kFull-timePosted 5d ago

About the role

Role focused on building post-training and agent systems for public-sector and regulated-industry settings, turning workflows into tasks, environments, evaluations, and reward signals, and deploying systems within operator boundaries.

Responsibilities

  • Applied Research at Prime: turn workflows into tasks, environments, evaluations, and reward signals; run post-training experiments against real baselines; deploy what works; determine if gains come from model, data, harness, inference, or infrastructure; reuse parts as shared research, open-source systems, and product capabilities.
  • Role scope: work 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.
  • You should 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: 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.

Skills

Required:RL / post-trainingPrivate ML systemsAgent safety / high-assurance evaluationPython

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
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Prime Intellect

Commoditizing Compute & Intelligence

Prime Intellect democratizes AI development at scale. Our platform makes it easy to find global compute resources and train state-of-the-art models through distributed training across clusters. Collectively own the resulting open AI innovations, from language models to scientific breakthroughs.

AI developmentbessemer-ai-infrastructure-2026compute resourcesdistributed trainingmodelsreinforcement-learning-platformsscience
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