Senior AI/ML Testing Engineer (Hybrid)

Electronic Arts

Vancouver

Ibrido

CAD 120.000 - 180.000

Tempo pieno

14 giorni+

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Vantaggi offerti da questo lavoro

Extended health/dental/vision

Retirement plan

Three weeks vacation

Descrizione del lavoro

Electronic Arts in Burnaby, BC is seeking an experienced Software Engineer for FC Quality Verification Engineering. You will help build automated testing frameworks, evaluation tools using AI/ML, and integrate tests into CI/CD pipelines to improve test coverage for EA SPORTS FC.

The role requires 5+ years in software engineering, 2+ years with AI/ML/LLMs, Python proficiency, and experience with NLP. This hybrid role offers collaboration with cross-functional teams and a path to advance in a

Competenze

5+ years of professional software engineering experience, including 2+ years of hands-on experience building, integrating, or evaluating AI/ML and LLMs-based systems.
Master of Science in Computer Science, Mathematics or a related field, or equivalent professional engineering experience.
In-depth experience with LLMs, generative AI, and NLP concepts, architectures, and failure modes.
Experience developing evaluation frameworks for generative systems, including dataset curation, model-as-a-judge design, validation criteria, error analysis, and quantitative/qualitative metrics.
Proficiency in Python, algorithms, data structures, modular design, automated testing, debugging, and version control.
Experience preparing datasets, defining validation criteria, and performing error analysis using quantitative ML metrics.
Experience with natural language processing or large language models, such as prompting, fine-tuning, or retrieval-augmented generation.

Mansioni

You will build automated testing frameworks, validation checks, and CI/CD pipeline integrations with reproducible diagnostics.
Develop methods that help us explore complex software behaviour, identify coverage gaps, and detect defects.
Build automated checks to validate software behaviour and data against requirements.
Develop evaluation frameworks for generative AI and LLMs-based systems (e.g., implementing LLMs-as-a-judge patterns, rubric-based scoring, and automated calibration) alongside deterministic validation checks.
Apply generative AI, NLP, and machine learning, balancing heuristic, deterministic, and model-based approaches to solve complex software testing problems.
Integrate testing tools into development pipelines, producing reproducible diagnostics, and support the software you deliver.
Prototype latest machine learning and LLMs capabilities, and collaborate with engineering, design, and quality partners to clarify requirements, review code, and resolve blockers.