Unity Technologies
Technology
StaffMachineLearningEngineer,MLInfrastructure-Offline
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff Machine Learning Engineer, ML Infrastructure - Offline at Unity Technologies. Skills: ML Infrastructure, Offline ML platform, Distributed training. Design data pipelines. Operate data pipelines”
Industry & Context.
Systems thinking
No relocation support, No work visa sponsorship
What They're Looking For.
Must Have
Experience building large-scale ML pipelines, Experience with distributed computing frameworks, Programming skills in Python, Experience working with large-scale distributed workloads, Experience with modern data infrastructure, Systems thinking, Proven ability to lead technical direction, Sufficient knowledge of English
Nice to Have
Familiarity in the Ray ecosystem, Experience building infrastructure for training data generation, Experience building infrastructure for dataset preparation, Experience building infrastructure for ML feature pipelines, Deep experience designing and operating production-grade data pipelines
What You'll Do.
Design data pipelines
Operate data pipelines
Generate training datasets
Develop infrastructure for distributed training
Integrate ML pipelines with orchestration systems
Improve reproducibility of ML pipelines
Improve observability of ML pipelines
Optimize performance across compute systems
Optimize resource utilization
Partner with ML engineers
Enable large-scale experimentation
Enable model iteration
Lead architectural improvements
How You'll Work.
Team & Collaboration
ML engineers; Platform teams
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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