Remote Principal AI Engineer — Reinforcement Learning

Placements24; Randfontein; Remote; ZAR 2,501,000 - 3,501,000; Full time

Benefits offered by this job

Remote stipend

Health, dental, vision insurance

Retirement plan

Professional development budget

Flexible working hours

Job summary

Placements24 is seeking a Principal AI Engineer to lead RL research and development in a fully remote role. You will design, implement, and deploy cutting-edge RL algorithms, collaborating with a global team of researchers and engineers. The position offers flexibility, strong mentorship, and opportunities to influence AI strategy across projects.

You will guide junior engineers, publish findings, and ensure scalable, efficient RL systems with robust evaluation methods in diverse environments.

Responsibilities

• Lead the research and development of advanced Reinforcement Learning algorithms for real-world applications.
• Design, implement, and optimize RL models for simulation and deployment in complex, dynamic environments.
• Develop robust training methodologies and evaluation frameworks for RL agents.
• Collaborate remotely with cross-functional teams to understand requirements and deliver AI-driven solutions.
• Contribute significantly to the company’s technical strategy and intellectual property in RL.
• Mentor and provide technical guidance to junior engineers and researchers in a remote setting.
• Publish technical documentation, research papers, and present findings to internal and external audiences.
• Ensure the scalability, efficiency, and reliability of deployed RL systems.

Job description

Placements24 is seeking a Principal AI Engineer to lead RL research and development in a fully remote role. You will design, implement, and deploy cutting-edge RL algorithms, collaborating with a global team of researchers and engineers. The position offers flexibility, strong mentorship, and opportunities to influence AI strategy across projects.

You will guide junior engineers, publish findings, and ensure scalable, efficient RL systems with robust evaluation methods in diverse environments.