Staff ML Engineer: Scalable Offline ML Pipelines

Unity Technologies; Mountain View (CA); On-site; USD 210,000 - 284,000; Full time

Benefits offered by this job

Health insurance

Commute subsidy

Employee stock ownership

Retirement plans

Vacation and personal days

Parental leave support

Office snacks

Mental Health programs

Employee resource groups

Training and development

Job summary

Unity Technologies is seeking a Staff ML Engineer to design and evolve a large-scale offline ML platform. You will build reliable infrastructure for training datasets, orchestrate ML workflows, and enable distributed model training at scale.

Collaborate with ML engineers and platform teams to ensure pipelines scale with growing data and complexity. You will shape how datasets are prepared and how models are trained, validated, and delivered to distributed training systems, while ensuring

Qualifications

• Experience building large-scale ML pipelines.
• Experience with distributed frameworks like Ray, Spark, Flink.
• Strong Python programming for large-scale workloads.
• Experience with data infrastructure and orchestration systems.

Responsibilities

• Design and operate large-scale data pipelines to generate training datasets.
• Develop infrastructure for distributed training workflows with PyTorch, Ray Data, and Ray Train.
• Integrate ML pipelines with Flyte, Airflow to enable multi-stage training workflows.
• Improve reproducibility and observability via validation and testing.
• Optimize performance and resource use across distributed systems.
• Lead collaboration with ML engineers to enable large-scale experimentation.
• Drive architectural improvements for scalable offline ML pipelines.

Skills

Large-scale ML pipelines

Ray ecosystem

Data generation pipelines

Production pipelines

Python

Data infrastructure

Systems thinking

Technical leadership

Tools

Flyte

Airflow

Ray Data

Ray Train

Job description

Unity Technologies is seeking a Staff ML Engineer to design and evolve a large-scale offline ML platform. You will build reliable infrastructure for training datasets, orchestrate ML workflows, and enable distributed model training at scale.

Collaborate with ML engineers and platform teams to ensure pipelines scale with growing data and complexity. You will shape how datasets are prepared and how models are trained, validated, and delivered to distributed training systems, while ensuring