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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 post-training models at scale. The company has raised $150M from Founders Fund, Radical Ventures, NVIDIA, and prominent AI operators including Andrej Karpathy and leaders from Perplexity, Harvey, Cognition, OpenAI, and others.
In this Applied Research role, you will build post-training and evaluation systems for multimodal agents that operate browsers, desktops, and cross-application workflows. Computer-use agents must understand changing interfaces, maintain intent over long workflows, recover from mistakes, manage permissions and side effects, and verify genuine task completion.
Your work will be grounded in an active external program with real customers and research partners. You will turn workflows and traces into environments, rewards, and experiments that produce reliable improvement. The research challenge is distinguishing genuine capability gains from artifacts of data, training harness, inference, or infrastructure.
Key responsibilities include:
- Building long-horizon computer-use environments from partner workflows while preventing shortcuts, data leakage, and unsafe side effects
- Training agents to recover from interface changes, partial failures, and ambiguous intermediate states
- Developing evaluations that distinguish genuine task completion from plausible-looking behavior (correctness, efficiency, permissions, appropriate escalation)
- Owning multimodal environments built from demonstrations, tools, interface state, and outcome criteria
- Building browser, desktop, and cross-application agents with reliable sandbox and boundary enforcement
- Running training and evaluation experiments from demonstrations, interventions, production failures, synthetic tasks, and verifiable outcomes
- Experimenting on recovery and generalization across applications, layouts, permissions, and deployment contexts
Every Applied Research role at Prime Intellect is externally facing; you will work directly with customers, users, or research partners while maintaining a substantive research agenda.
REQUIREMENTS:
- Relevant experience in multimodal agents, computer use, browser or desktop automation, reinforcement learning, imitation learning, human-computer interaction, 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 willingness to change direction when evidence changes
- Strong Python skills and 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 valued: publications, open-source systems, benchmarks, or shipped products demonstrating difficult work, failures, and how evidence shaped your approach
