Create evaluations and establish evaluation frameworks to measure both technical/practical performance and non-deterministic performance like EQ Research & implement novel prompting techniques Spin the data flywheel to extract insights from extensive language datasets to uncover issues and opportunities to improve model response quality. Accountable to own the status of key projects, proactively identifying risks and proposing solutions to ensure timely delivery. 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. Master'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 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. 3+ years experience working in a fast-paced environment, managing multiple priorities, and adapting to changing requirements and deadlines. Hands-on experience with prompt design, context window management, and model evaluation. 2+ years of experience shipping consumer-facing products. You've brought products to market, collected user feedback, defined success metrics and iteratively improved products. 1+ years of experience in building LLM applications with familiarity in agent and orchestration frameworks, tool use, LLM evaluations and driving efficiency improvements. Technical depth in software development, data science and machine learning. While you are not expected to write code on the critical path, you're able to navigate and contribute to code repositories to implement context engineering improvements and write LLM scorers and classifiers to evaluate model response quality.
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