● Ship - Increase velocity of ML model deployment into production through automation of model<br /> management, deployment, and rollout processes.<br /> ● Validate - Increase confidence of model rollouts by enriching and automating model validation prior<br /> to and immediately after deployment.<br /> ● Measure - Provide insight into accuracy and relevance of ML model predictions in production by<br /> measuring and monitoring model input and output data distributions, as well as user<br /> engagement/feedback on predictions.<br /> ● Automate - Incorporate user feedback/activity into new ML model training by automation of data<br /> collection, model retraining, model measurement, etc., towards a goal of continuous automated<br /> model retraining.<br /> ● Build - Provide internal tools or incorporate commercial tools (e.g. Kubeflow, VertexAI, LangChain,<br /> LangSmith, etc) into data scientist workflows for data analysis, feature generation, model<br /> development, etc., to boost ML team productivity.<br /> ● Collaborate - Bridge the gap between ML research and production-grade backend code by working<br /> with other engineering teams to integrate new ML models or APIs into production.<br /> About you (don't worry if you don't have this whole list- we expect you to learn with us):<br /> ● A self-starter, with the interest and passion to contribute in a fast-paced startup environment.<br /> ● Provide technical leadership and help drive the team’s ML direction & vision<br /> ● B.S. degree or equivalent in Computer Science, Mathematics, or similar field of study.<br /> ● Professional experience as a full-time machine learning engineer.<br /> ● 5+ years of experience building ML products<br /> ● 3+ years of experience using Large Language Models in production<br /> ● Strong proficiency in software development and system design<br /> ● Fluent in Python<br /> ● Experienced with common Python data science libraries such as PyTorch, HuggingFace,<br /> Pandas, numpy and scikit-learn<br /> ● Experienced with the lifecycle of model training, evaluation and deployment<br /> ● Experienced with using SQL<br /> Preferences:<br /> ● Experienced building APIs in Python, particularly in FastAPI or Flask.<br /> ● Experienced with using Pytest, Docker, and sqlalchemy<br /> ● Experienced with MLOps platforms such as KubeFlow or MLFlow