Frame complex product and customer problems as applied science opportunities with clear success metrics. Design, build, and evaluate machine learning and AI models, algorithms, and experimentation frameworks. Develop robust data, training, evaluation, and inference pipelines suitable for production use. Conduct rigorous offline and online experiments; analyze results and communicate actionable recommendations. Partner with software engineers and product managers to integrate scientific solutions into products and services. Improve model quality, reliability, efficiency, safety, and responsible AI practices throughout the lifecycle. Provide technical leadership through design reviews, mentoring, and raising the scientific bar for the team. Stay current with relevant research and identify opportunities to apply new methods to customer needs. Bachelor's Degree in Statistics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., generative AI, agentic evaluation, LLM, research) OR Master's Degree in Statistics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., generative AI, agentic evaluation, LLM, research) OR Doctorate in Statistics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., generative AI, agentic evaluation, LLM, research) These requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., generative AI, agentic evaluation, LLM, research) OR Doctorate in Statistics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience(e.g., generative AI, agentic evaluation, LLM, research) OR equivalent experience. Experience building, deploying, and operating production machine learning systems at scale. Experience with deep learning, generative AI, large language models, natural language processing, recommender systems, computer vision, or another area relevant to the hiring team. Experience with responsible AI, model safety, privacy, fairness, robustness, and evaluation. Strong written and verbal communication skills, including communicating technical results to diverse stakeholders. Evidence of technical leadership through mentoring, cross-team collaboration, patents, or publications. Experience in Privacy-Preserving ML, Synthetic Data Generation, and LLM/Agentic Evaluation would be valuable.