Sr. Reinforcement Learning Researcher

Symbolic Mind is seeking a Sr. RL Researcher to join our innovative team. We're looking for an experienced professional with a strong background in developing and implementing cutting-edge reinforcement learning algorithms, particularly focusing on RLHF techniques.

At Symbolic Mind, you'll have the opportunity to work on challenging problems at the forefront of AI and contribute to the development of next-generation intelligent systems.

Responsibilities:

• Design and implement advanced RLHF and deep reinforcement learning algorithms.
• Develop and optimize RL models for complex tasks.
• Collaborate with cross-functional teams to integrate RL solutions into our products.
• Stay current with the latest advancements in RLHF and RL research.

Requirements:

• Degree in Computer Science, Machine Learning, or related field.
• 3+ years of hands-on experience in implementing RLHF and deep reinforcement learning algorithms.
• Strong programming skills in Julia or Python and proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow).
• Comprehensive knowledge of RL algorithms, including PPO, A2C, DQN, and their variants.
• Experience with large-scale machine learning projects and distributed computing.
• Excellent problem-solving skills and ability to innovate in the RL space.
• Strong communication skills and ability to work in a collaborative environment.

Preferred Qualifications:

• Research & publishing experience
• Contributions to RL projects or open-source libraries.
• Experience with building, fine tuning/RAG of LLMs.
• Familiarity with evolutionary algorithms and multi-agent reinforcement learning.
• Track record of applying RL techniques to real-world problems.

Benefits

• Competitive salary and benefits package, with a clear path for career advancement.
• An inclusive, multicultural team environment that values knowledge sharing and open dialogue.
• Equity participation, aligning your success with the company's growth.

Details:

• Full-time role
• Remote position (slight preference for candidates in PST time zone)