Staff ML Infrastructure Engineer, Large-Scale Pipelines

Unity Technologies; New York (NY); Hybrid; USD 210,000 - 284,000; Full time

Benefits offered by this job

Comprehensive health, life, and dental

Stock ownership

Generous vacation

Mental Health programs

Job summary

Unity Technologies seeks a staff ML engineer to design and evolve an offline ML platform that builds training datasets, orchestrates ML workflows, and enables scalable distributed model training.

You will collaborate with ML engineers and platform teams to ensure reliability, scalability, and cost-efficiency of pipelines. The role emphasizes architectural leadership and performance optimization.

Qualifications

• Design and operate large-scale data pipelines for ML training and experimentation.
• Experience with distributed computing frameworks and Ray ecosystem for data processing and training.
• Proven ability to build infrastructure for training data generation, dataset prep, or ML feature pipelines.
• Deep experience designing and operating production-grade data pipelines.
• Strong Python skills and experience with large-scale distributed workloads.
• Experience with modern data infra (data lakes, warehouses, orchestration, streaming).
• Ability to lead technical direction and influence architecture across teams.

Responsibilities

• Design and operate large-scale data pipelines that generate training datasets for ML training and experimentation.
• Develop infrastructure supporting distributed training workflows (e.g., Ray Data, Ray Train).
• Integrate ML pipelines with workflow orchestration systems (Flyte, Airflow) for multi-stage training.
• Improve reproducibility and observability of ML pipelines via validation and testing.
• Optimize performance and resource use across distributed compute for data processing and training.
• Partner with ML engineers to enable large-scale experimentation and model iteration.
• Lead architectural improvements to keep offline ML pipelines scalable and cost-efficient.

Skills

Large-scale pipelines

Ray / Spark / Flink

Python programming

Data infrastructure

Distributed training

Systems thinking

Technical leadership

Tools

Ray Data

Ray Train

Flyte

Airflow

Job description

Unity Technologies seeks a staff ML engineer to design and evolve an offline ML platform that builds training datasets, orchestrates ML workflows, and enables scalable distributed model training.

You will collaborate with ML engineers and platform teams to ensure reliability, scalability, and cost-efficiency of pipelines. The role emphasizes architectural leadership and performance optimization.