Senior ML Data Infrastructure Engineer — Scalable Pipelines

Unity; Mountain View (CA); On-site; USD 200,000 - 261,000; Full time

Benefits offered by this job

Comprehensive health insurance

Employee stock ownership

Competitive retirement plans

Generous vacation and personal days

Commuting subsidy

Parental leave support

Training and development programs

Job summary

Unity is seeking a Senior Machine Learning Engineer, Data Infrastructure, to design and evolve large-scale offline data pipelines in Mountain View. You will build reliable infrastructure for generating training datasets and orchestrating workflows with ML engineers and platform teams.

You will own end-to-end pipeline reliability, scalability, and cost-efficiency, working across batch and streaming workloads to support model training, experimentation, and production ML systems.

Qualifications

• Experience with Flink, Spark, and Ray for distributed data processing.
• Experience building infrastructure for training data generation and ML feature pipelines.
• Ability to optimize big data pipelines for cost and performance.

Responsibilities

• Develop infrastructure for batch and stream big data processing using Flink, Spark, Ray.
• Design and operate large-scale data pipelines for training datasets.
• Integrate pipelines with Flyte, Airflow, or similar orchestration systems.
• Improve observability with dataset validation, monitoring, and automated testing.
• Optimize performance and resource use across distributed compute systems.
• Collaborate with ML engineers to enable large-scale experimentation.
• Lead architectural improvements for scalable, reliable offline pipelines.

Skills

Flink

Spark

Ray

Python

Distributed data processing

Tools

Flyte

Airflow

Job description

Unity is seeking a Senior Machine Learning Engineer, Data Infrastructure, to design and evolve large-scale offline data pipelines in Mountain View. You will build reliable infrastructure for generating training datasets and orchestrating workflows with ML engineers and platform teams.

You will own end-to-end pipeline reliability, scalability, and cost-efficiency, working across batch and streaming workloads to support model training, experimentation, and production ML systems.