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 6+ years related experience (e.g., statistics, predictive analytics, research) Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g. machine learning, deep learning or similar technologies) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 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, finetuning, RAG), 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. Demonstrated ability to lead cross‑disciplinary efforts (PM, ENG, UXR, editorial/policy) from idea to shipped impact; mentoring scientists and setting technical vision. 2+ years of experience working with recommender systems/ranking or content‑quality/safety models at consumer scale, with clear business impact. 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.
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