Machine Learning Engineer (Performance)

Hathora is a real-time compute platform designed for latency-sensitive workloads, powering applications from global multiplayer games to next-gen real-time AI inference. Our platform enables engineering teams to deploy and scale dedicated infrastructure in minutes while maintaining exceptional performance through advanced regional coverage, optimized cold starts, and optimized network routing. Utilizing proprietary Elastic Metal technology, Hathora integrates the performance of bare metal with the scalability of cloud solutions, ensuring cost-efficient and reliable operations for demanding workloads. Founded by engineers from leading tech companies like Databricks and Palantir, Hathora is trusted by studios and startups to deliver global, always-on infrastructure. Developers can focus on creating innovative applications, knowing Hathora's infrastructure has them covered.

This is a full-time remote role for a Machine Learning Engineer (Performance). The engineer will focus on researching, designing, and optimizing machine learning models with a strong emphasis on enhancing performance and efficiency. Responsibilities include implementing cutting-edge algorithms, developing and training neural networks, analyzing large-scale data, and applying pattern recognition techniques. Close collaboration with cross-functional teams to ensure optimal model deployment and scalability will also be a key part of the role.

• Solid understanding of Pattern Recognition and Neural Networks
• Strong foundational knowledge in Computer Science and Statistics
• Proficiency in designing and implementing Algorithms for machine learning
• Experience with analyzing and optimizing large-scale machine learning systems
• Strong programming skills and familiarity with relevant tools and frameworks
• Ability to work collaboratively within a distributed, remote team environment
• Bachelor's or higher degree in Computer Science, Artificial Intelligence, Mathematics, or a related field
• Previous experience with performance tuning and optimization in real-time applications is a plus