Applied Research - Robotics

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.

You will build environments, evaluations, agent systems, and post-training methods with an external robotics team. You will train and evaluate agents with reinforcement learning, world modeling, imitation learning, planning, and multimodal perception, using feedback from simulation and deployed robots. The experiments will test behavior under partial observations, long horizons, changing environments, and hardware and safety constraints.

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  Simulation and real-world environments for training, evaluation, and careful sim-to-real comparison.

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  Long-horizon agents that perceive, plan, act, remember, and recover under hardware constraints.

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  Training that combines offline and online RL, demonstrations, interventions, and outcome feedback.

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  Rewards and evaluations for task success, safety, adaptation, and continual improvement without silent regressions.

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.

• Build aligned simulation and real-world evaluations for a long-horizon embodied task, then measure exactly where policies fail to transfer.

• Combine demonstrations, interventions, offline data, and online feedback into a training loop that improves behavior without erasing prior competence.

• Train an embodied agent to recover from partial failures, changing scenes, and uncertain state while respecting safety and hardware constraints.

• Relevant experience: robotics, embodied AI, RL, imitation learning, control, world models, simulation, computer vision, agent evaluation, or production systems interacting with the physical world.

• 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.

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.