Applied Research - Computer Use

Prime Intellect, Inc.; New York (NY); A distancia; USD 150.000 - 300.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Cash compensation range $150-300k +equ

Flexible work locations (SF, NY,London

Professional development budget

Descripción de la vacante

Prime Intellect, Inc. is building the open superintelligence stack, unifying compute, environments, evaluations and deployment into one full-stack platform for frontier AI research.

The role focuses on applied research that turns workflows into tasks, environments, evaluations, and reward signals, with externally facing duties and collaboration with customers and partners. Strong Python skills and the ability to build reusable artifacts are required.

Formación

• Hands-on experience building and debugging model-training loops.
• Strong Python and the ability to build artifacts that others can run, inspect, and extend.
• Evidence of exceptional work via publications, open-source systems, benchmarks, or shipped products.

Responsabilidades

• Build and debug multimodal environments built from workflows, demonstrations, tools, and evaluation criteria.
• Externally facing role collaborating with customers, users, or research partners while pursuing a research agenda.
• Develop evaluations that distinguish genuine completion from plausible-looking behavior and improve reliability.

Conocimientos

Python

Model training

Experimentation

Descripción del empleo

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.

Role Impact

Responsibilities

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.

Representative early problems

Build long-horizon computer-use environments from real workflows while preventing shortcuts, leakage, and unsafe side effects.

Train agents to recover from interface changes, partial failures, and ambiguous intermediate states instead of restarting or silently drifting.

Develop evaluations that distinguish genuine completion from plausible-looking behavior, including correctness, efficiency, permissions, and appropriate escalation.

What you will own

Multimodal environments built from partner workflows, demonstrations, tools, interface state, and outcome criteria.

Training and evaluation from demonstrations, interventions, production failures, synthetic tasks, and verifiable outcomes.

Experiments on recovery and generalization across applications, layouts, permissions, and deployment contexts.

Requirements

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)
• Professional Development budget