Senior Machine Learning Engineer - Applied Scientist

leadership to align science priorities with product strategy, roadmap decisions, customer needs, and execution plans. Work with software engineering teams to produce AI systems with strong attention to reliability, scalability, latency, observability, maintainability, and MLOps best practices. Drive fast iteration cycles by forming hypotheses, defining metrics, running experiments, interpreting results, and recommending clear product or technical actions. Establish quality bars and decision-making frameworks for AI launches, including evaluation scorecards, regression testing, guardrail metrics, and responsible AI considerations. Mentor scientists and engineers, raise the technical bar, and influence cross-team best practices for AI development, experimentation, and evaluation. Communicate findings, tradeoffs, risks, and recommendations clearly to technical and non-technical stakeholders. 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) OR Master’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) PhD in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Significant experience building, evaluating, and shipping AI/ML-powered products or services. Hands-on experience with large language models, including prompt engineering, RAG, agents, tool use, model comparison, failure analysis, and evaluation-driven iteration. Deep understanding of LLM evaluation throughout the development lifecycle, including dataset creation, annotation strategies, automated and human evaluation, benchmark design, regression testing, launch criteria, and production monitoring. Experience designing offline experiments and interpreting results to inform model, system, and product decisions. Experience evaluating multimodal AI systems or AI experiences involving multiple input/output modalities. Solid understanding of product success metrics and ability to translate customer/business goals into measurable scientific and technical objectives. Solid software engineering skills and experience collaborating with engineers to build production-quality AI systems.