Senior ML Engineer — Platform & Integrations

About the role

This role involves developing and maintaining machine learning systems at a global scale within a dynamic gaming industry environment. The position offers a chance to work with innovative ML techniques such as reinforcement learning and imitation learning to advance game understanding agents and optimize ML infrastructure.

Responsibilities

• Build and operate distributed training systems for game AI agents across multi-node GPU clusters, integrating with RL/IL frameworks.
• Design and manage large-scale game simulation environments for parallel rollouts, experience collection, and policy evaluation.
• Develop automated ML pipelines for version control, validation, and experiment tracking of models and data.
• Build serving systems focusing on model inference, including version routing, fallback behavior, and latency/accuracy optimization.
• Create developer tools, SDKs, and templates to streamline the agent development and deployment process, gathering feedback to validate adoption.
• Define system health indicators, drift detection metrics, training diagnostics, and establish alerting protocols.
• Produce comprehensive documentation for models and systems, ensuring transparency and ease of understanding.
• Manage and resolve production incidences like training instability, reward degradation, and policy regressions.
• Collaborate with security, compliance, and privacy teams to implement governance and security controls.
• Mentor junior ML engineers, participate in recruitment efforts, and contribute to interview processes.

Requirements

• Bachelor's degree in Computer Science, related field, or equivalent experience.
• 3+ years of software engineering experience, with substantial ML engineering, platform, or systems expertise.
• Proven ability to operate distributed systems in production effectively.
• Experience with distributed training of ML agents, including RL, IL, or other paradigms at scale.
• Proficiency in Python and working knowledge of C++, with a willingness to engage with game engine technologies (e.g., Unreal Engine).
• Hands-on experience with ML frameworks (e.g., PyTorch), distributed RL/IL training frameworks (e.g., Ray/RLlib).
• Experience with GPU workloads, multi-node orchestration, and resource optimization.
• Ability to work across the entire stack, from training algorithms to production infrastructure.
• Strong written communication skills for system documentation and standards.

Qualifications

• Experience with game simulation or AI is a plus.
• Background in inference serving, model evaluation frameworks, or ML governance is advantageous.
• Prior success in building greenfield platforms or self-service ML capabilities is a plus.
• Passion for player experience, games, or innovative technology.

Benefits

• Competitive compensation package
• Full Benefits; Medical, Vision, Dental, and more!
• Opportunity to get in with an industry leading organization.