R&D Engineer

Job Description Job Summary UBISOFT is seeking an Intermediate R&D Engineer to prototype and validate new technologies that improve game testing and quality control. This role focuses on building rapid prototypes and running experiments using GenAI, VLMs (Vision-Language Models), and AI/ML to explore automation opportunities across QC. You will work closely with the R&D Manager and cross-functional partners to translate ideas into measurable experiments, evaluate feasibility, and iterate toward proof-of-value prototypes. This role provides an excellent opportunity to gain hands-on experience in software development, automation, and quality control processes within the gaming industry. Job Duties / Responsibilities Core Responsibilities - Build rapid prototypes using GenAI/VLM/ML to address QC and testing pain points. - Design and execute experiments: define hypotheses, datasets/inputs, evaluation criteria, baselines, and success metrics (accuracy, precision/recall, latency, cost, robustness). - Implement data pipelines for experiments: data collection, cleaning, labeling support, and dataset versioning (as needed for proofs-of-concept). - Explore and compare approaches and document trade-offs. - Integrate prototypes with existing internal tools/test systems at a lightweight level (APIs, CLIs, plugins) to enable user trials and feedback. - Analyze experiment results, identify failure modes, and iterate quickly based on findings and stakeholder input. - Maintain reproducible research practices: experiment tracking, configuration management, and clear documentation of assumptions and limitations. - Collaborate in agile workflows: estimate assigned tasks, provide status updates, and incorporate feedback iteratively. Learning Opportunities - Gain hands-on experience with software development and automation tools. - Develop a deeper understanding of software testing lifecycles. - Enhance coding and debugging skills in a collaborative environment. - Learn about CI/CD pipelines and version control systems. Core Competencies - Eager to Learn: Highly motivated and quick to grasp new concepts. - Experimental Mindset: Comfortable with ambiguity; able to form hypotheses and learn quickly through iteration. - Pragmatic Engineering: Delivers prototypes that are usable and measurable, without over-engineering. - Analytical Thinking: Interprets results critically; understands bias, variance, and common evaluation pitfalls. - Collaboration: Works well with technical and non-technical stakeholders; communicates trade-offs clearly. - Adaptability: Keeps pace with rapidly evolving AI/ML tooling and best practices. Behavioral Competencies - Curiosity: Passion for exploring new technologies and their applications. - Teamwork: Ability to collaborate effectively with diverse teams. - Adaptability: Willingness to take on new challenges and adapt to changing priorities.