Applied Research - Robotics

Role Impact

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.

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 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.

What 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 that combines offline and online RL, demonstrations, interventions, and outcome feedback.
• Rewards and evaluations for task success, safety, adaptation, and continual improvement without silent regressions.

Requirements

• 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.
• Evidence of exceptional work — publications, open-source systems, benchmarks, and shipped products. 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