Unity Technologies

SeniorMachineLearningEngineer,MLInfrastructure-Offline

$650–1000k ~AI est. Shanghai, China
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Machine Learning Engineer, ML Infrastructure - Offline at Unity Technologies”

Full Job Description

The opportunity Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company. Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform also supports large-scale model training, feature generation, and experimentation workflows that power production ML systems. To support this growth, we need strong technical ownership to ensure our ML pipelines remain reliable, scalable, and architecturally sound. The Role We are seeking a senior ML engineer to design and evolve the large-scale offline platform. This role focuses on building reliable infrastructure for generating training datasets, orchestrating ML workflows, and enabling efficient, distributed model training at scale. You will work closely with ML engineers and platform teams to ensure our pipelines can efficiently handle growing data volumes and increasingly complex training workloads. You will play a key role in shaping how model datasets are prepared as well as model training, validated, and delivered to distributed training systems, while ensuring the reliability, scalability, and performance of our offline ML platform. What you'll be doing Design and operate large-scale data pipelines that generate training datasets used for machine learning training and experimentation Develop infrastructure that supports distributed training workflows using technologies such as Pytorch, Ray Data, and Ray Train, etc. Integrate ML pipelines with workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflows Improve reproducibility and observability of ML pipelines through dataset validation, monitoring, and automated testing Optimize performance and resource utilization across distributed compute systems used for data processing and model trainin

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