Senior ML Engineer, Data Infrastructure & Pipelines

Unity Enterprise; Mountain View (CA); Hybrid; USD 200,000 - 261,000; Full time

Benefits offered by this job

Comprehensive health insurance

Life and disability insurance

Employee stock ownership

Competitive retirement plans

Generous vacation and personal days

Commute subsidy

Job summary

Unity is seeking a Senior/Staff Machine Learning Engineer, Data Infrastructure in Mountain View, CA to design and evolve a large-scale offline data platform supporting data pipelines, training datasets, and experimentation workflows.

You will work with ML engineers and platform teams to ensure scalable, reliable infrastructure for model training and data generation, while optimizing cost and performance.

Qualifications

• Experience with distributed computing frameworks (Flink/Spark/Ray) for data processing.
• Experience building infrastructure for training data generation or ML feature pipelines.
• Experience optimizing big data pipelines for cost efficiency.

Responsibilities

• Develop infrastructure that supports batch and stream big data processing using Flink, Spark, Ray, etc.
• Design and operate large-scale data pipelines that generate training datasets for ML training.
• Integrate data pipelines with workflow orchestration systems (Airflow, Flyte) for multi-stage training workflows.
• Improve reproducibility and observability through dataset validation and automated testing.
• Optimize performance and resource utilization across distributed compute systems.
• Partner with ML engineers to enable large-scale experimentation and model iteration.
• Lead architectural improvements to ensure offline data pipelines are scalable and cost-efficient.

Skills

Distributed computing

Python

Large-scale data processing

Tools

Flink

Spark

Ray

Airflow

Flyte

Job description

Unity is seeking a Senior/Staff Machine Learning Engineer, Data Infrastructure in Mountain View, CA to design and evolve a large-scale offline data platform supporting data pipelines, training datasets, and experimentation workflows.

You will work with ML engineers and platform teams to ensure scalable, reliable infrastructure for model training and data generation, while optimizing cost and performance.