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Our Quality Verification and Standards (QVS) team is an important part of our development process, consistently delivering actionable insights that support our game teams to optimize software performance and elevate gameplay. Who do we work with? You will work closely with the Battlefield Franchise
Battlefield QV We are Battlefield Quality Verification (QV), an integral part of Electronic Arts, known for delivering the highest quality gaming experiences in the industry. We believe that phenomenal games start with diverse teams, mirroring the players and communities we serve.
As a Software Developer within the Quality Verification Engineering (QVE) Team, you will report to the Engineering Manager. As a member of the worldwide Quality Verification (QV) organization, you will help ensure that our games will be stable through every stage of development. You will be part of a distributed team, located in Stockholm, Guilford, Bucharest, Los Angeles, and Montreal. You will work with other teams to analyze requirements, problem areas and translate them into technical designs, which are reviewed and agreed upon by relevant parties. You will work hybrid at the office located in Montreal.
Responsibilities
- Design, implement, and maintain AI bot systems for automated multiplayer playtesting, ensuring realistic simulation of player behaviour across game modes and maps.
- Develop and iterate bot behavior systems - including movement, combat, interaction, and coordination - using a hierarchical architecture shared across titles.
- Collaborate with designers and QA engineers to translate gameplay scenarios into testable bot behaviours, using visual scripting‑based authoring tools.
- Build telemetry pipelines that capture detailed per‑bot event data, enabling downstream analysis, regression testing, and behaviour validation.
- Investigate and resolve bot failures surfaced during automated playtest sessions, including pathfinding edge cases, network desynchronization, and complex gameplay system interactions.
- Contribute to architecture modernization efforts, refactoring monolithic systems into composable, maintainable patterns and eliminating cross‑module duplication.
- We welcome prior ML experience, but do not require it, as you participate in exploratory research on machine learning‑driven bot behaviours, including early‑stage reinforcement learning experiments.
- Review peers' code, provide constructive feedback, and contribute to shared documentation and engineering best practices.