Machine Learning Engineer, Applied Research and Model Development

About Tome Tome is a unified platform for enterprise sellers and account managers. We use state-of-the-art models to simplify complex research and strategic planning for sellers. Tome can surface the most actionable knowledge about a customer from within internal systems as well as from public information across thousands of data sources. Our system is tuned and customized by a team of experienced sellers, engineers, and researchers. Many of us worked on large-scale products at Llama, Messenger, Instagram, and LinkedIn before this. We design and build Tome in close partnership with our early customers-many of whom are mature enterprise sales orgs. We'll continue to work this way to ensure we build an enduring solution to longstanding problems in the industry. About the role Tome's AI/ML team builds the experiences at the core of our product, developing new applications to wow our customers. Today, the team focused on building a powerful, domain-specific AI that outperforms generic LLMs We're inspired by the challenge of creating innovative new AI products for people doing serious work, and we're looking to grow our AI/ML team to meet that challenge. What you'll do Create and ship magical, highly-differentiated AI experiences that sales teams can't live without Craft Tome's AI/ML strategy in tight collaboration with founders and execs Pioneer the training of new models that leverage both historical data and synthetic training data Prototype innovative, LLM-powered experiences, and drive their development into robust product features Help build a world class AI/ML engineering team by recruiting and mentoring teammates Who you are You have 5 years of industry experience in NLP, with a strong portfolio of model training You have a strong understanding of deep learning AI/ML frameworks or cloud services You have hands-on ML Ops experience You possess deep expertise in NLP and model training, specifically with LLMs Proven experience adapting open-source generative models for specific use cases, demonstrating a deep understanding of its architecture and capabilities Bonus Points Proven track record of leading successful AI/ML research projects in a product environment Publications in applied AI/ML scientific journals Experience navigating open source/vendor solutions in LLM ops space (Langchain, Llama, Pinecone, etc)