[Hiring] Applied Research - Computer Use @Prime Intellect

Role Description

Computer-use agents must understand changing interfaces, maintain intent over long workflows, recover from mistakes, manage permissions and side effects, and know when an outcome is actually complete. You will build post-training and evaluation systems for multimodal agents operating browsers, desktops, and cross-application workflows. The work is grounded in an active external program. The research challenge is turning real workflows and traces into environments, rewards, and experiments that produce reliable improvement.

Responsibilities

• 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.
• Browser, desktop, and cross-application agents with reliable sandbox and boundary enforcement.
• 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.

Qualifications

• Relevant experience: multimodal agents, computer use, browser or desktop automation, RL, imitation learning, HCI, embodied agents, or long-horizon agent evaluation.
• 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

• 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