Applied Research - Computer Use

Prime Intellect AI

San Francisco (CA)

Remote

USD 150,000 - 300,000

Full time

4 days ago

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Benefits offered by this job

Cash compensation

Equity incentives

Flexible work schedule

Visa sponsorship

Relocation support

Professional development budget

Team off-sites & conferences

Job summary

Prime Intellect AI is building an open frontier AI stack. We design post-training, evaluation, and deployment systems for multimodal agents that operate across browsers, desktops, and cross-application workflows.

The role blends research with practical software engineering to deliver reliable improvements. The Applied Research team collaborates with customers and partners, turning workflows into reusable research components and product capabilities.

Qualifications

Experience with multimodal agents and long-horizon evaluation.
Hands-on experience building model-training and evaluation loops.
Strong experimental judgment with clear failure attribution and adaptability.
Strong Python and ability to build reproducible artifacts.

Responsibilities

Turn workflows into tasks, environments, evaluations, and reward signals.
Run post-training experiments against real baselines and deploy effective results.
Work directly with customers, users, or research partners while pursuing a substantive research agenda.

Skills

Multimodal agents

Browser automation

Desktop automation

Reinforcement learning

Imitation learning

HCI

Embodied agents

Long-horizon evaluation

Python

Experiment design

Tools

Open research tooling

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

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

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.
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.

Requirements

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 and Perks