Senior ML Data Infrastructure Engineer — Scale ML Pipelines

Unity; Mountain View (CA); In loco; USD 166.000 - 273.000; Tempo pieno

Vantaggi offerti da questo lavoro

Health insurance

Stock options

Retirement plans

Paid time off

Parental leave

Employee training programs

Descrizione del lavoro

Unity is seeking a Senior Machine Learning Engineer for Data Infrastructure to design and evolve a large-scale offline data platform. You will own infrastructure that generates training datasets and orchestrates data workflows, partnering with ML engineers to scale experimentation and model training.

The role focuses on reliable, scalable pipelines across batch and streaming workloads, with opportunities to lead architectural improvements and optimize cost-efficiency while aligning with Unity’s

Competenze

• Proficient in building data infrastructure for training datasets and ML workflows.
• Experience with large-scale offline data platforms and batch/stream processing.
• Ability to optimize pipelines for cost, reliability, and performance.

Mansioni

• Develop infrastructure for batch and stream processing (Flink, Spark, Ray).
• Design data pipelines to generate training datasets for ML models.
• Integrate pipelines with workflow engines (Airflow, Flyte) for multi-stage training.
• Improve reproducibility and observability via validation and monitoring.
• Optimize distributed compute resources for performance and cost.
• Collaborate with ML engineers to enable large-scale experimentation.
• Lead architectural improvements for scalable, reliable offline pipelines.

Conoscenze

Python

Distributed computing

ML feature pipelines

Data lakes/warehouses

System performance & scalability

Leadership without authority

Strumenti

Flink

Spark

Ray

Descrizione del lavoro

Unity is seeking a Senior Machine Learning Engineer for Data Infrastructure to design and evolve a large-scale offline data platform. You will own infrastructure that generates training datasets and orchestrates data workflows, partnering with ML engineers to scale experimentation and model training.

The role focuses on reliable, scalable pipelines across batch and streaming workloads, with opportunities to lead architectural improvements and optimize cost-efficiency while aligning with Unity’s