About TomeTome 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 roleTome'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 doCreate and ship magical, highly-differentiated AI experiences that sales teams are eager to use
Craft Tome's AI/ML strategy in tight collaboration with founders and execs
Identify user needs that are well suited for AI/ML solutions, frame the problems, and collaborate with product leaders on solutions
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 areYou have a BS or MS degree in CS, AI, or applied math
You have 5+ years of experience building AI/ML based products in NLP domains
You have a strong understanding of deep learning AI/ML frameworks or cloud services
You have hands-on ML Ops experience
Bonus PointsExperience leading AI/ML product initiatives
Publications in applied AI/ML scientific journals
Experience navigating open source/vendor solutions in LLM ops space (LangChain, Llama, Pinecone, etc)
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