Unity has 38 active AI-related job listings, with a significant focus on agents, representing 32% of the roles, and serving infrastructure at 29%. The majority of these positions are within the Engineering function, with hiring concentrated in the United States. Frequent technical tags include model_serving, inference_infra, and agent_orchestration, suggesting a direction toward operationalizing AI models. In the last 30 days, Unity has posted 27 new AI roles, a substantial increase of 440% compared to the previous 30-day period.
Currently tracking 35 active AI roles, down 46% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $100k–$300k (avg $197k).
Unity currently has 53 active AI-related roles in our index. The most common open titles are: Senior Machine Learning Engineer, Advertiser Growth (4), Staff Machine Learning Engineer, ML Infrastructure (4), Machine Learning Engineer, Next-Generation Recommendation Systems (New Grad / PhD) (3), Senior Data Scientist (3), Senior Machine Learning Infrastructure Engineer (3). Most positions are in Engineering and Research.
Unity's active AI hiring is concentrated in: agents (36%), serving infrastructure (28%), data (19%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Unity is hiring AI talent in: United States (40 roles), Canada (5 roles), Israel (4 roles), China (4 roles).
Job postings at Unity most frequently reference: model serving, inference infra, recommender systems, agent orchestration, llm observability.
In the past 30 days, Unity has posted 13 new AI-related roles. That is a -62% change versus the prior 30 days (34 → 13).
| Title | Stage | AI score |
|---|---|---|
| ML Infrastructure Engineer - (Early Career/Internship) ML Engineer role focused on building and maintaining the offline ML platform infrastructure for data pipelines, distributed model training, and ML workflows. This role supports large-scale model training, feature generation, and experimentation, bridging research and production at scale. | Data | 7 |
| Staff Machine Learning Engineer, ML Infrastructure - Offline Staff ML Engineer focused on building and evolving the large-scale offline ML platform for data generation, workflow orchestration, and distributed model training at Unity. | Data | 7 |
| Senior Machine Learning Engineer, ML Infrastructure - Offline Senior ML Engineer focused on building and operating a large-scale offline ML platform for Unity. The role involves designing and evolving data pipelines for training datasets, orchestrating ML workflows, and enabling efficient, distributed model training. Key responsibilities include developing infrastructure for distributed training, integrating with orchestration systems, and optimizing performance. |
| DataServe |
| 7 |
| Staff Machine Learning Engineer, ML Infrastructure Staff ML Engineer focused on building and operating a large-scale offline ML platform for data generation, feature engineering, and distributed model training at Unity. | Data | 7 |
| Staff Machine Learning Engineer, ML Infrastructure Staff ML Engineer focused on building and operating a large-scale offline ML platform for Unity, supporting data pipelines, distributed model training, and experimentation workflows. | Data | 7 |
| Staff Machine Learning Engineer, ML Infrastructure Staff ML Engineer focused on building and evolving a large-scale offline ML platform for data pipelines, distributed model training, and feature generation at Unity. | Data | 7 |
| Staff Machine Learning Engineer, ML Infrastructure Staff ML Engineer focused on building and evolving a large-scale offline ML platform for data pipelines, distributed model training, and feature generation at Unity. | Data | 7 |