Apply Edge Tailor my CV for this job — free

AI Assurance Engineer - MLE Focus

TikTok USDS Joint Venture · San Jose, CA

Tailor my CV for this job — free

See similar jobs

ResponsibilitiesAbout the TeamDetection Engineering is part of Content Assurance and is aligned with USDS JV Security & Privacy. The team provides product-independent technical assurance for recommender and generative AI systems, testing whether system behavior remains within declared safety, quality, and governance boundaries before and after release.AI Assurance Engineering builds and operates shared evaluation infrastructure, including test harnesses, benchmark pipelines, replay systems, adversarial-testing tools, safety-metric telemetry, and assurance CI/CD. The team is independent from the product teams whose models it evaluates. About the RoleAs an AI Assurance Engineer, you will design rigorous evaluation methods and production-quality capabilities for measuring and diagnosing recommender and generative AI behavior. You will develop benchmark datasets, define reproducible test protocols, implement safety, quality, robustness, and drift metrics, and investigate behavioral regressions through adversarial testing and counterfactual replay. This role focuses on evaluation engineering and technical assurance, not product model training, policy setting, moderation, or release ownership.Responsibilities- Evaluation Methods and Benchmarks: Define reproducible protocols, benchmark suites, sampling strategies, datasets, and statistical analyses for recommender and generative AI evaluations.- Behavioral Testing and Diagnosis: Test safety, ranking quality, consistency, adversarial robustness, refusal behavior, leakage, factuality within scope, drift, and regressions.- Evaluation Platforms and Signals: Implement evaluation code, metrics, replay workflows, and assurance signals on shared infrastructure while preserving traceability and repeatability.- Design Evaluation Methodologies: Define measurable evaluation objectives, metrics, sampling plans, baselines, acceptance criteria, and reproducible protocols for pre-release and post-release assurance of recommender and generative AI systems.- Engineer Benchmark Datasets: Curate, version, document, and quality-check benchmark and adversarial datasets, including harm-exposure samples, ranking-quality cases, jailbreak prompts, leakage tests, and counterfactual replay inputs.- Implement Behavioral Tests: Build evaluation code for recommender objectives and GenAI behavior, including safety-category coverage, ranking quality, fairness and consistency, refusal correctness, factuality within scope, adversarial robustness, provenance signals, and drift detection.- Diagnose Evaluation Results: Apply statistical and experimental rigor to distinguish model-behavior regressions from data, sampling, or infrastructure effects; analyze post-release drift against release-time baselines and produce traceable technical findings.- Contribute Production-Quality Platforms: Integrate benchmarks, metrics, replay tests, and red-team assets with shared evaluation runtimes and assurance CI/CD; improve reproducibility, telemetry quality, and failure diagnosis without assuming ownership of the underlying platform methodology validation.Qualifications

Minimum Qualifications

Preferred Qualifications