Staff ML Engineer, Scalable Offline Pipelines

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

Benefits offered by this job

Health insurance

Life insurance

Disability insurance

Commute subsidy

Employee stock ownership

Retirement plans

Vacation days

Parental leave

Office snacks

Mental health programs

Employee Resource Groups

EAP program

Training & development

Volunteer programs

Job summary

Unity Technologies seeks a staff ML engineer to design and evolve an offline ML platform that powers training data generation, model training at scale, and experimental workflows across the company.

You will collaborate with ML engineers and platform teams to build reliable, scalable pipelines, improve reproducibility, and lead architectural improvements for large-scale ML workflows in production environments.

Qualifications

• Experience building large-scale ML pipelines.
• Experience with distributed computing frameworks (Ray, Spark, Flink).
• Proficient in Python for large-scale workloads.
• Experience with data lakes, warehouses, orchestration systems.

Responsibilities

• Design and operate large-scale data pipelines that generate training datasets for ML training and experimentation.
• Develop infrastructure that supports distributed training workflows (Ray Data, Ray Train, PyTorch).
• Integrate ML pipelines with workflow orchestration systems (Flyte, Airflow) for reliable multi-stage training.
• Improve reproducibility and observability through validation, monitoring, and tests.
• Optimize performance and resource use across distributed compute systems.
• Lead architectural improvements to ensure scalability and cost-efficiency.
• Collaborate with ML engineers to enable efficient large-scale experimentation and model iteration.

Skills

Large-scale ML pipelines

Distributed computing

Ray Data/Train

Training data infrastructure

Production data pipelines

Python programming

Data infrastructure

Distributed systems

Technical leadership

Tools

Spark

Flink

Ray

Flyte

Airflow

Job description

Unity Technologies seeks a staff ML engineer to design and evolve an offline ML platform that powers training data generation, model training at scale, and experimental workflows across the company.

You will collaborate with ML engineers and platform teams to build reliable, scalable pipelines, improve reproducibility, and lead architectural improvements for large-scale ML workflows in production environments.