דרושים RL Researcher בכל הארץ

Job Title: RL Researcher

Job Type: Full-time, Core Team

Location: Remote

The AI platform for human intelligence—we build models that rigorously vet PhDs and scale human data training for frontier LLMs.

Our agent, Zara, autonomously sources and evaluates researchers and data trainers—powering post-training (RLHF, evals, red-teaming) for top AI labs.

Now building a research team to break new ground in RL — setting the pace for the next wave of AI alongside our AI Lab customers.

Job Summary

Join our team as an RL Researcher, focusing on Reinforcement Learning Environments. You’ll work on designing, developing, and experimenting with the next generation of RL environments that support cutting-edge AI research and applications. With nearly every major AI Lab among our customers, this role directly contributes to advancing global AI research efforts.

What You’ll Do

• Conduct research and experimentation in RL environments, from foundational design to deployment in large-scale applications.
• Collaborate with AI Labs, industry experts, and internal teams to align research initiatives with emerging AI needs.
• Contribute to publications, experiments, and prototypes that push the boundaries of RL environment development.
• Translate research findings into practical improvements to our environment frameworks and tools.
• Stay informed on the latest advancements in RL, simulation, and AI, and apply them to ongoing projects.

What We’re Looking For

• Proven expertise in RL environments (simulation frameworks, benchmarks, or environment design) with a strong research or applied project portfolio.
• Solid technical foundation in Python and RL/simulation tools (e.g., OpenAI Gym, Mujoco, Isaac Gym, Unity ML-Agents).
• Strong collaboration skills and ability to communicate complex research clearly to technical and non-technical audiences.
• Self-motivated and comfortable in a remote, dynamic, and high-impact environment.

Preferred

• PhD in Computer Science, Machine Learning, or a related field (or equivalent applied research experience).
• Contributions to open-source RL environments or collaborations with leading AI Labs.
• Publications, talks, or research contributions in the AI or RL community.