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# AI Researcher - Reinforcement Learning

- **Company:** 1X, LLC
- **Location:** San Carlos, CA, United States
- **Salary:** $200,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Information Engineering, Python (Programming Language), Reinforcement Learning, Pytorch, Build Tools, Codebase
- **Published:** June 18, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1a06e785d93fba81

## About the Role

Do you have experience in Simulated training environment?, * Strong Python and/or C++ with experience in large codebases and build tools (Bazel or equivalent)

 * Proficiency with PyTorch for RL policy training and experimentation
 * Hands-on experience with simulation platforms (Isaac Sim, MuJoCo, or equivalent) for policy training at scale
 * Demonstrated experience training RL policies for manipulation or locomotion tasks, including addressing the sim-to-real gap on physical hardware
 * Preferred Skills
 * Experience with model-based RL or world-model-guided policy learning that leverages predictive models to improve sample efficiency
 * Familiarity with imitation learning or learning from demonstration (behavior cloning, GAIL, IQL) as a complement or bootstrap to RL
 * Experience deploying RL-trained policies to physical robots in production environments, including monitoring, failure analysis, and iterative improvement
 * Background in legged locomotion, dexterous manipulation, or contact-rich control for physical systems

## Description

* Train and deploy RL policies for manipulation and locomotion tasks that perform reliably in real-world home environments measured by field task success rates, not just simulation benchmarks
 * Advance sim-to-real transfer techniques that measurably narrow the gap between simulation training performance and real-world policy behavior, enabling faster iteration cycles
 * Build training and evaluation infrastructure that lets the team iterate on policies faster with standardized benchmarks, automated regression detection, and clear connections between training metrics and field performance
 * Partner with hardware, controls, data, and QA teams to ship RL-trained skills to production customer sites, owning the handoff from research to deployment

Key Competencies

 * Sim-to-real practitioner closing the sim-to-real gap on physical systems; understands domain randomization, reward shaping, and the engineering required to make simulated policies transfer reliably to real hardware
 * RL algorithms depth with strong foundation in RL algorithms (PPO, SAC, TD-MPC, or similar); can choose the right approach for the task and modify or extend it when standard methods fall short
 * Full-stack ownership owning data engineering, model architecture, and deployment; treats a promising training curve as the beginning of the job, not the end
 * Effective cross-functional partner working closely with hardware, controls, QA, and data teams to translate RL research into deployed robot skills, and communicates technical constraints clearly across disciplines


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