Machine Learning Researcher

Role Overview:

Must-Have Skills & Experience:

• Deep Expertise in Reinforcement Learning:

   A strong theoretical and applied background in RL, including areas like deep RL, multi-agent RL, imitation learning, and model-based RL.

• Agentic Systems & Reasoning:

   Proven experience in designing and building agents that can reason, plan, and execute complex tasks.

• Simulation & World Modeling:

   Experience in creating or utilizing simulation environments (e.g., Isaac Gym, MuJoCo, custom simulators) for training agents.

• Strong Programming & ML Skills:

   Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow/JAX.

• Research Mindset:

   A track record of identifying challenging problems, formulating hypotheses, and running rigorous experiments to validate them.

Good-to-Have Skills & Experience:

• Experience with large-scale distributed training for RL agents.
• Familiarity with causal inference and its application to agent learning.
• Knowledge of game theory and its application in multi-agent systems.
• Experience with language models as part of an agent's reasoning or planning loop.

What Gets Us Immediately Excited:

• High-Impact Publications:

   A strong publication record in top-tier AI conferences (e.g., NeurIPS, ICML, ICLR, CoRL) with a focus on RL, robotics, or agentic systems.

• Novel RL Algorithms or Environments:

   Demonstrable creation of a new RL algorithm, a complex simulation environment, or an agent that achieved state-of-the-art performance on a challenging benchmark.

• Success in AI Competitions:

   Top rankings in recognized AI or robotics competitions (e.g., Kaggle, RoboCup, NeurIPS competitions).