Applied Research - Sovereign AI

Prime Intellect, Inc.; United States; Remote; USD 150,000 - 300,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation support

Flexible work

Professional development budget

Team off-sites & conferences

Job summary

Prime Intellect, Inc. is building the open superintelligence stack and seeking researchers who can advance post-training systems and agent workflows for regulated domains.

You will operate inside partner boundaries to develop secure, auditable, and high-assurance AI capabilities with real-world impact. Ideal candidates bring hands-on experience with RL/post-training loops, privacy-preserving evaluations, and Python artifacts that other researchers and engineers can run and extend.

Qualifications

• Experience in RL/post-training, private ML systems, high-assurance evaluation, or deployment under strict data and infrastructure constraints.
• Hands-on experience building and debugging model-training, post-training, agent, evaluation loops.
• Strong experimental judgment with meaningful baselines and trace inspection.
• Excellent Python skills and ability to build shareable artifacts for researchers and engineers.
• Comfort owning ambiguous problems and keeping partners updated as you progress.

Responsibilities

• Develop private improvement loops and post-training workflows within operator boundaries.
• Build agent systems with permissions, traceability, and bounded tools.
• Design evaluations for reliability, calibration, and compliance in regulated settings.
• Explore how privacy and infrastructure constraints affect learning and transfer insights across settings.
• Collaborate with customers and partners while maintaining a clear research agenda.

Skills

Reinforcement learning

Post-training systems

Agent safety

Regulated-domain ML

Public-sector deployment

Data & infrastructure constraints

Job description

Own Your Intelligence

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 - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together, AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

Role Impact

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. They also need the ability to improve those systems on their own workflows rather than depend indefinitely on a closed external model. You will build post-training and agent systems in public-sector and regulated-industry settings.

Applied Research at Prime

We turn a workflow into tasks, environments, evaluations, and reward signals, run post-training experiments against real baselines, and deploy what works. When a result looks good, we find out whether the gain came from the model or from the data, harness, inference, or infrastructure. The reusable parts become shared research, open-source systems, and product capabilities.

Every Applied Research role is externally facing. Most researchers 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.

Requirements

• 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

We value publications, open-source systems, benchmarks, and shipped products as evidence of exceptional work. Show us something difficult you built, what failed, and how the evidence changed your approach.

Benefits And Perks

• 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

If you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.