Artificial Intelligence Engineer

Company DescriptionJulia is a San Francisco-based company specializing in building general medical artificial intelligence and medical robots for private clinics and hospitals. The founders bring experience from UT, NASA, MIT, and Harvard Medical School, ensuring a high level of expertise and innovation in the development of cutting-edge medical technologies.Role DescriptionThe AI Engineer will be responsible for the development and implementation of pattern recognition algorithms, neural networks, software development, and natural language processing (NLP) techniques to create advanced medical AI solutions and robots.LLM Frameworks: Demonstrated expertise working with Large Language Models such as OpenAI’s GPT series (GPT-3, GPT-4)--including model integration, customization, and optimization within diverse applications.LLM Fine-Tuning and Transfer Learning: Proven experience in fine-tuning LLMs for specific tasks or industries, including the use of transfer learning techniques to adapt pre-trained models to new domains or requirements.Lead the development of Julia's support chatbot & LLM system: Applying LLM, active learning, semi- supervised learning, weak label generation, documentation embedding/retrieval and data augmentation strategies to improve the consumer, dasher, and merchant support experienceDrive the personalization of Julia's issue prediction & resolution policies: Using both personalization, recommendation and dynamic pricing modeling technologies to serve millions of customers on personalized prediction resolution for any issues they might encounter during their journeySpearhead the creation of next-generation LLM AI Agent tools: Building Co-pilot system to evolve how millions of users interact with our support systemApply stratification, variance reduction, and other advanced experiment design techniques to create A/B tests to efficiently measure the impact of your innovations while minimizing risk to the broader systemQualificationsExperience with LLM refinement and vector database embeddingsProficiency with common ML and data platforms such as AzureML, Amazon SageMaker, Databricks, and SnowflakeKnowledge of AI/ML pipelines, AI/MLOps concepts and toolsAbility to build production-grade AI/ML solutions with scalability in mindExperience with MLOps tools and techniques to optimize ML lifecycle managementExperience with ML metadata and artifact tracking platforms such as MLflowExperience containerizing and deploying models and solutions to cloud platforms like Azure or AWSUnderstanding of model governance concepts such model risk analysis, QA, complianceExperience with building ML technical architecture diagrams encompassing data, model building, operationsExperience with operating end-to-end ML platforms supporting analytics and ML teamsExperience with assessing model technical debt, maintaining pipelines, keeping systems up-to-dateExperience with Python for analytics and ML applicationsProficiency with common Python data analysis libraries like NumPy, Pandas, SciPyExperience with common Python ML libraries like Scikit-Learn, TensorFlow, PyTorchExperience with Jupyter Notebooks for ML experimentation and prototypingAbility to transition ML prototypes to production solutionsExperience with Terraform for IaC of ML infrastructure on Azure, AWS cloud platforms.Pattern Recognition and Neural Networks expertiseStrong background in Computer Science and Software DevelopmentExperience with Natural Language Processing (NLP)Ability to work collaboratively in multidisciplinary teamsExcellent problem-solving and analytical skillsKnowledge of healthcare industry standards and regulationsMaster's or Ph.D. in Computer Science, Artificial Intelligence, or related field