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Salary: USD 150,000 - 300,000 / annual
Prime Intellect is building the open superintelligence stack—infrastructure for frontier AI labs to train and deploy models at scale. The company has raised $150M from Founders Fund, Radical Ventures, NVIDIA, and prominent AI operators including Andrej Karpathy and leaders from OpenAI, Databricks, Perplexity, and others.
As an Applied Research Scientist focused on Sovereign AI, you will build post-training and agent systems for national institutions, public-sector organizations, and regulated industries that require strict boundaries around data, deployment, access, auditability, and human authority. Your work bridges frontier research, real infrastructure, and go-to-market for a nascent category.
You will own end-to-end improvement loops that operate within partner constraints—often on their infrastructure and under their access rules. Representative problems include: building private improvement loops that turn sensitive traces into training signals and deployable model updates without crossing data boundaries; training tool-using domain agents with permissioned, traceable, recoverable actions evaluated against real procedures; and developing high-assurance evaluations for reliability, calibration, robustness, human escalation, and compliance under adversarial conditions.
Your ownership spans improvement loops from trace selection to deployed update, agents with explicit permissions and evidence trails, evaluations testing reliability and compliance with real procedures, and experiments on how privacy and infrastructure constraints affect learning across high-assurance settings.
Every Applied Research role is externally facing—you will work directly with customers, users, or research partners while maintaining a substantive research agenda. When results look promising, you isolate whether gains came from the model, data, harness, inference, or infrastructure, and convert reusable parts into shared research, open-source systems, and product capabilities.
REQUIREMENTS:
- Relevant experience in one or more of: 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: ability to design meaningful baselines, inspect traces carefully, attribute failures clearly, and change direction when evidence warrants.
- Strong Python proficiency and ability to build artifacts that other researchers and engineers can run, inspect, and extend.
- Comfort owning ambiguous problems and keeping partners and teammates informed as you resolve them.
- Evidence of exceptional work valued: publications, open-source systems, benchmarks, or shipped products demonstrating difficult work, failures, and how evidence shaped your approach.
