Staff Machine Learning Engineer

Unity3D is the world’s leading game engine, powering play for more than 3 billion consumers each month. They are seeking a senior ML engineer to design and evolve their large-scale offline platform, focusing on building reliable infrastructure for generating training datasets and enabling efficient, distributed model training at scale.

Responsibilities:

• Design and operate large-scale data pipelines that generate training datasets used for machine learning training and experimentation
• Develop infrastructure that supports distributed training workflows using technologies such as Pytorch, Ray Data, and Ray Train, etc
• Integrate ML pipelines with workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflows
• Improve reproducibility and observability of ML pipelines through dataset validation, monitoring, and automated testing
• Optimize performance and resource utilization across distributed compute systems used for data processing and model training
• Partner closely with ML engineers to enable efficient large-scale experimentation and model iteration
• Lead architectural improvements to ensure our offline ML pipelines remain scalable, reliable, and cost-efficient

Requirements:

• Strong experience building large-scale ML pipelines
• Experience working with distributed computing frameworks such as Ray, Spark, Flink and familiarity in the Ray ecosystem (Ray Data, Ray Train) for distributed data processing and model training
• Experience building infrastructure for training data generation, dataset preparation, or ML feature pipelines
• Deep experience designing and operating production-grade data pipelines
• Strong programming skills in Python and experience working with large-scale distributed workloads
• Experience with modern data infrastructure (data lakes, warehouses, orchestration systems, streaming platforms)
• Strong systems thinking, with the ability to reason about performance, scalability, reliability, and cost tradeoffs in distributed systems
• Proven ability to lead technical direction and influence architectural decisions across teams without formal authority