Unity has 49 active AI-related job listings. The majority of these roles are focused on agents, representing 35% of the total. Engineering is the dominant function, with 46 positions. The United States is the primary hiring country, accounting for 37 of the listings. Frequent tech tags include model_serving, recommender_systems, and agent_orchestration. In the last 30 days, there were 0 new AI roles posted, a 100% decrease compared to the prior 30 days.
Currently tracking 38 active AI roles, down 39% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $100k–$300k (avg $197k).
Unity currently has 49 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 Machine Learning Infrastructure Engineer (3), Staff Software Engineer, Feature Platform (3). Most positions are in Engineering and Research.
Unity's active AI hiring is concentrated in: agents (35%), serving infrastructure (27%), data (22%). 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 (37 roles), China (5 roles), Canada (4 roles), Israel (3 roles).
Job postings at Unity most frequently mention: Performance Optimization, A/B Testing, Model Monitoring, Data Pipelines, Apache Airflow.
In the past 30 days, Unity has posted 0 new AI-related roles. That is a -100% change versus the prior 30 days (9 → 0).
| Title | Stage | AI score |
|---|---|---|
| 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. |
| 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 Software Engineer, Feature Platform Staff Software Engineer on the Feature Platform team at Unity, responsible for building and operating infrastructure that powers machine learning, experimentation, and optimization for their ads ecosystem. This involves designing and operating systems that transform high-volume event data into production-grade feature datasets for bidding, attribution, and ranking, working at the intersection of distributed systems, platform engineering, and ML infrastructure. The role owns the full software lifecycle of pipeline systems, supporting both offline training and online feature serving. | DataServe | 5 |
| Staff Software Engineer, Feature Platform Staff Software Engineer on the Feature Platform team at Unity, responsible for building and operating infrastructure that powers machine learning, experimentation, and optimization for their ads ecosystem. This involves designing and operating systems that transform high-volume event data into production-grade feature datasets for bidding, attribution, and ranking, working at the intersection of distributed systems, platform engineering, and ML infrastructure. The role owns the full software lifecycle of pipeline systems, supporting both offline training and online feature serving. | DataServe | 5 |
| Staff Software Engineer, Feature Platform Staff Software Engineer on the Feature Platform team at Unity, responsible for building and operating infrastructure that powers machine learning, experimentation, and optimization for the ads ecosystem. This involves designing and operating systems that transform high-volume event data into production-grade feature datasets for bidding, attribution, and ranking, working at the intersection of distributed systems, platform engineering, and ML infrastructure. The role owns the full software lifecycle of pipeline systems, from architecture to reliability and performance, supporting both offline training and online feature serving. | DataServe | 5 |