Staff Machine Learning Engineer, Applications

SmithRx is a rapidly growing, venture-backed Health-Tech company dedicated to transforming the pharmacy benefit management sector. They are seeking a Staff Machine Learning Engineer to drive AI innovation and implement AI-powered solutions, collaborating with cross-functional teams to enhance productivity and quality through generative AI capabilities.

Responsibilities:

• Design Generative AI solutions, such as information retrieval and summary generation for support and operations organizations
• Partner with Software Engineering teams to build and deploy GenAI applications
• Ideate and explore opportunities to deploy GenAI technologies for customer-facing experiences
• Drive the adoption and scalability of Generative AI capabilities within SmithRx; advocacy on the technology and evangelize the use of GenAI

Requirements:

• 5+ years of experience in data science, machine learning, and AI development, with proven success leading AI initiatives
• MS or PhD in Computer Science, Electrical Engineering, Statistics, Robotics or equivalent fields
• Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning)
• Strong mathematical background (linear algebra, calculus, probability, and statistics)
• Proficiency in Python and object-oriented programming
• Strong experience working with machine learning and natural language processing techniques and tools
• Strong experience using Generative AI models, with a good understanding of deep learning model classes such as GPT, VAE, and GANs, as well as their hyperparameters
• Strong experience with retrieval methods e.g. using embeddings
• Strong experience using key Python packages for data wrangling, machine learning and deep learning such as pandas, sklearn, TensorFlow, torch, transformers, LangChain, etc
• Experience in Prompt Engineering and few-shot techniques to enhance LLM's performance on specific tasks
• Experience with training and fine-tuning deep learning models, especially LLMs, and how to tune hyperparameters to ensure task generalization
• Ability to drive a project and work both independently and within a cross-functional team
• Excellent verbal and written communication, able to articulate complex concepts with a non-technical audience
• Experience with embedding model training and retrieval method evaluation approaches
• Experience with LLM architectures, adapters, Mixture of Experts (MoEs) pretraining and fine-tuning techniques
• Experience with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches
• Experience with reinforcement learning approaches in the context of fine-tuning LLM outputs
• Experience with time series analysis and multivariate time series modeling