Remote Reinforcement Learning Research Engineer

Placements24; Queenstown (MD); Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Competitive salary with bonuses

Comprehensive health insurance and 401

Fully remote role with flexible hours

Professional development and access to

Conference attendance

Job summary

Placements24 is seeking a Remote AI Research Engineer focused on Reinforcement Learning (RL). You will design, implement, and experiment with advanced RL algorithms from anywhere, tackling problems in robotics, optimization, and game AI.

You will publish findings, collaborate with a global team, and contribute to open-source RL libraries while improving efficiency on large-scale simulations and real systems.

Qualifications

• PhD or MS in CS/AI with RL focus.
• Publications or notable open-source RL contributions.
• Strong RL theory: Q-learning, Policy Gradients, Actor-Critic, Deep RL.
• Proficiency in Python and DL libraries (TensorFlow, PyTorch).
• Experience with simulation environments: OpenAI Gym, MuJoCo, PyBullet.
• Excellent communication for remote collaboration.
• Ability to work independently on remote projects.

Responsibilities

• Conduct cutting-edge RL research and publish findings.
• Develop, implement, and test RL agents/environments in Python using TF/PyTorch.
• Design experiments to evaluate scalability of RL algorithms.
• Collaborate with a globally distributed team of researchers and engineers.
• Contribute to open-source RL libraries and tools.
• Analyze results and provide actionable insights.

Skills

Research RL fundamentals

Theoretical RL knowledge

Remote collaboration

Education

PhD or MS with RL publications

Tools

Python

TensorFlow

PyTorch

OpenAI Gym

MuJoCo

PyBullet

Job description

Placements24 is seeking a Remote AI Research Engineer focused on Reinforcement Learning (RL). You will design, implement, and experiment with advanced RL algorithms from anywhere, tackling problems in robotics, optimization, and game AI.

You will publish findings, collaborate with a global team, and contribute to open-source RL libraries while improving efficiency on large-scale simulations and real systems.