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The Company

Our client is an innovative gaming company focused on creating high‑quality, immersive experiences powered by advanced technology. They combine strong engineering with creative design to develop games enjoyed by players worldwide, and are expanding their AI capabilities to push the boundaries of gameplay and simulation.

Role Overview

We are looking for an engineer who can take ownership of the full lifecycle of Reinforcement Learning (RL) systems-from model design and experimentation through large‑scale training and deployment. This position suits someone who enjoys working at the intersection of research, software engineering, and cloud computing, and who thrives in environments with complex data and evolving requirements.

What You’ll Do

• Develop RL solutions end‑to‑end, covering data pipelines, model design, training, and production deployment.
• Run RL and ML experiments using Python‑based machine learning frameworks.
• Implement, customize, and tune RL algorithms such as PPO, SAC, and DQN.
• Architect RL systems using modern cloud tools and distributed infrastructure.
• Improve RL training pipelines to maximize performance, scalability, and reliability.

Requirements (Must‑Have)

• Several years of experience delivering RL systems that have been used in real‑world or production environments.
• Deep understanding of core RL algorithm families and training workflows.
• Strong hands‑on experience with Python and frameworks like PyTorch or TensorFlow.
• Experience working with real RL datasets, including data formats, schemas, and collection processes.
• Proven ability to write production‑quality machine learning code.

Bonus Skills (Nice to Have)

• Experience with distributed RL frameworks (e.g., Ray RLlib).
• Familiarity with cloud platforms, with preference for experience on Google Cloud.
• Background in RL applied to computer vision.
• Exposure to backend engineering, DevOps, or MLOps infrastructure.

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The Role:
You will design, train, optimize, and productionize Reinforcement Learning models at scale. This role is ideal for someone who is comfortable operating across research, engineering, and cloud systems while owning end‑to‑end RL development. You will work with complex datasets, ambiguous requirements, and modern distributed systems.
Key Responsibilities:

• Build RL solutions from data collection to deployment.
• Run ML and RL experiments using Python ML libraries.
• Implement and tune RL algorithms such as PPO, SAC, or DQN.
• Design RL architectures using cloud tooling.
• Optimize RL pipelines for speed, scale, and reliability.

Requirements (Must Have):

• Multiple years experience delivering production or real‑world RL solutions.
• Strong knowledge of RL algorithm families and training procedures.
• Hands‑on experience with Python, PyTorch or TensorFlow.
• Experience working with real RL datasets and associated data schemas.
• Experience shipping production‑ready ML code.

Nice to Have:

• Distributed RL (Ray RLLib).
• Cloud platforms (GCP preferred).
• CV‑based RL experience.
• Backend engineering or MLOps exposure.

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