Machine Learning Engineer, ML/GenAI Evaluation

What you'll do

• Define evaluation criteria and quality metrics for ML models powering Wallet features
• Design adversarial test strategies to identify model failure modes before they reach users
• Develop robustness testing methodologies including distribution shift, out-of-distribution generalization, and temporal drift
• Own end-to-end fairness evaluation by defining metrics and building bias test suites across protected attributes
• Synthesize evaluation results into clear insights guiding model development priorities and product decisions
• Establish and maintain user persona–stratified benchmarks reflecting the diversity of Wallet's global user base

What we're looking for

• M.S. in Machine Learning, Computer Science, Statistics, Applied Mathematics, or related field preferred; 7+ years hands-on ML experience required.
• Deep expertise in model evaluation, offline metrics design, and behavioral testing for production systems.
• Proven ability to construct adversarial test suites and edge-case corpora that surface model failure modes.
• Strong programming skills in Python with fluency in evaluation tooling and data pipelines.
• Experience designing evaluation frameworks beyond accuracy/F1, including precision-recall tradeoffs, calibration, fairness.
• Excellent communication skills for translating metric results into product-quality narratives.