Senior AI Research Engineer - Deep Reinforcement Learning

Placements24; Sandton; Hybrid; ZAR 2,495,000 - 3,826,000; Full time

Benefits offered by this job

Remote-friendly culture

Home office stipend

Health insurance

Job summary

Placements24 is seeking a Senior AI Research Engineer specializing in Deep Reinforcement Learning to join our remote, globally distributed team. You will design and implement novel DRL algorithms, advance state-of-the-art research, and mentor junior researchers while contributing to publications and scalable systems.

The role requires a PhD or MSc, 5+ years in AI/ML, Python expertise, and hands-on experience with DRL frameworks and simulation platforms.

Qualifications

• Ph.D. or Master's in CS/AI or related field.
• 5+ years in AI/ML research with DRL focus.
• Experience building DRL algorithms (PPO, SAC, DQN, AlphaZero).
• Proficiency in Python and major DL frameworks.

Responsibilities

• Lead the design and implementation of novel DRL algorithms and frameworks.
• Apply DRL to autonomous control, optimization, and decision making.
• Develop simulations to train/evaluate DRL agents.
• Collaborate with cross-functional teams to integrate DRL solutions.
• Conduct experiments, analyze results, and optimize performance.
• Stay updated with DRL research and publish findings.
• Mentor junior researchers and engineers.
• Write production-ready Python code and maintain ML pipelines.
• Contribute to publications, patents, and conferences.
• Ensure safety, reliability, and scalability of DRL systems.

Skills

Deep RL expertise

Algorithm research

Python proficiency

Remote collaboration

Education

PhD or MSc in CS/AI

Tools

TensorFlow

PyTorch

OpenAI Gym

MuJoCo / Isaac Gym

Job description

About the Role

Our client is a renowned leader in pioneering advanced artificial intelligence research and applications. We are seeking a highly skilled and motivated Senior AI Research Engineer specializing in Deep Reinforcement Learning (DRL) to join our fully remote, globally distributed team. This is an exceptional opportunity to contribute to state-of-the-art research and development, tackling complex challenges in areas such as robotics, game AI, and sophisticated decision‑making systems. You will work alongside a team of world‑class scientists and engineers, leveraging cutting‑edge methodologies and high‑performance computing resources. This role demands a deep theoretical understanding of RL, combined with practical expertise in implementing and scaling DRL algorithms. As a remote‑first position, you will enjoy the flexibility to work from anywhere while making a significant impact on the future of AI innovation.

Key Responsibilities

• Lead the research, design, and implementation of novel Deep Reinforcement Learning algorithms and frameworks.
• Apply DRL techniques to solve complex, real‑world problems in domains such as autonomous control, optimization, and strategic decision‑making.
• Develop and maintain robust simulation environments for training and evaluating DRL agents.
• Collaborate with cross‑functional teams to integrate DRL solutions into cutting‑edge products and research projects.
• Conduct extensive experimentation, analyze results, and optimize agent performance and learning efficiency.
• Stay at the forefront of DRL research by reading academic papers, attending conferences, and experimenting with new techniques.
• Mentor junior researchers and engineers, fostering a culture of knowledge sharing and technical excellence.
• Write high‑quality, production‑ready code in Python and associated ML/DRL libraries.
• Contribute to research publications, patent applications, and presentations at leading AI conferences.
• Ensure the safety, reliability, and scalability of deployed DRL systems.

Requirements

• Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
• 5+ years of hands‑on experience in AI/ML research, with a significant focus on Deep Reinforcement Learning.
• Proven expertise in developing and implementing DRL algorithms (e.g., PPO, SAC, DQN, AlphaZero variants).
• Proficiency in Python and major deep learning frameworks (e.g., TensorFlow, PyTorch).
• Strong experience with RL libraries and simulation platforms (e.g., OpenAI Gym, MuJoCo, Isaac Gym).
• Deep theoretical understanding of reinforcement learning principles, Markov Decision Processes, and optimal control.
• Experience with large‑scale distributed training and MLOps practices is highly desirable.
• Excellent analytical, problem‑solving, and debugging skills.
• Strong communication and collaboration abilities, essential for remote teamwork.
• Ability to work independently and drive research initiatives forward.

Benefits

• Exceptional salary and performance bonus structure.
• Comprehensive remote work benefits, including home office setup stipend and co‑working space access.
• Full health, dental, and vision insurance coverage.
• Generous budget for professional development, including conferences, workshops, and advanced courses.
• Opportunity to work on groundbreaking research with direct impact.
• Complete flexibility in working hours and location.
• Access to state‑of‑the‑art computing infrastructure and cutting‑edge tools.
• A highly collaborative and intellectually stimulating global team environment.
• Regular virtual team events and opportunities for networking.