Lead content-quality understanding at scale. Design and deploy models that assess credibility, usefulness, freshness, safety, and diversity across modalities; reduce misinformation/toxicity error rates through prompt- and model-level innovations; build human-in-the-loop and active-learning pipelines that get better over time. Advance the recommendation & ranking stack. Architect and productionize large-scale DNN/LLM-enhanced recommenders (representation learning, sequence modeling, retrieval/ranking, slate optimization), balancing user satisfaction, content quality, and business goals. Own evaluation and experimentation. Define offline metrics (e.g., NDCG, ERR, calibration) and online methodologies (A/B tests, interleaving, counterfactual & bandit approaches) to confidently attribute impact and guard against regressions. Champion safety & trust. Partner with policy and platform teams to encode safety standards and editorial principles into the ML system; create red-teaming, adversarial, and safeguard layers for generative and curated experiences. Scale E2E ML systems. Collaborate with engineering on data contracts, feature stores, distributed training/inference, and automated rollout/rollback; drive architectural investments that increase agility and reliability of Discover's AI platform. Mentor & influence. Provide technical leadership across problem framing, methodology selection, code quality, and publishing/knowledge-sharing; uplevel peers through design reviews, deep-dives, and principled decision- Stay close to users. Translate user engagements and behavioral history into model objectives and product bets; ensure our AI solutions elevate relevance, transparency, and engagement for real users. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 2+ years of experience working with LLM, NLU or content-quality/safety models at consumer scale, with clear business impact. Have publications at top AI/ML conferences (e.g., KDD, SIGIR, EMNLP, NIPS, ICML, ICLR, RecSys, ACL, CIKM, CVPR, ICCV, etc.). Expertise with LLMs (prompting, RAG, Parameter-Efficient Fine-Tuning), multimodal modeling, and retrieval-augmented recommendation; familiarity with counterfactual learning and multi-objective optimization. Experience building content integrity/safety systems (e.g., misinformation, harmful content, low-quality/duplicate detection) and quality-aware ranking. 2+ years of experience in Python and at least one major deep learning framework (PyTorch/TensorFlow) with large-scale data processing and training/inference on distributed systems. 2+ years of evaluation & experimentation (offline metrics, A/B testing, bandits) and ML model development lifecycle.
