SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 150,000 - 300,000 / annual
Prime Intellect is building an open superintelligence stack—infrastructure that frontier AI labs build internally, now available to every ambitious AI team. The platform unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. The company is also developing open-source models trained end-to-end for long-horizon autonomous tasks.
In this Applied Research role, you will collaborate with an external robotics team to build environments, evaluations, agent systems, and post-training methods. You will train and evaluate agents using reinforcement learning, world modeling, imitation learning, planning, and multimodal perception, incorporating feedback from both simulation and deployed robots. Experiments will test behavior under partial observations, long horizons, changing environments, and hardware and safety constraints.
Applied Research at Prime Intellect turns workflows into tasks, environments, evaluations, and reward signals, then runs post-training experiments against real baselines. When results look promising, the team investigates whether gains came from the model, data, harness, inference, or infrastructure. Reusable insights become shared research, open-source systems, and product capabilities.
You will own: simulation and real-world environments for training, evaluation, and careful sim-to-real comparison; long-horizon agents that perceive, plan, act, remember, and recover under hardware constraints; training pipelines combining offline and online RL, demonstrations, interventions, and outcome feedback; and rewards and evaluations for task success, safety, adaptation, and continual improvement without silent regressions.
Every Applied Research role is externally facing—most researchers work directly with customers, users, or research partners while maintaining a substantive research agenda.
REQUIREMENTS:
- Relevant experience in 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 willingness to change direction when evidence changes.
- Strong Python 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, and shipped products demonstrating difficult work built, failures encountered, and how evidence changed your approach.
