Sumble is building a knowledge graph from web data with a first focus on data for go-to-market teams. We use sources like job posts and resume data to identify things like org structure, tech stack, and key projects (e.g., GenAI initiatives, cloud migrations). Our product already has strong product-market fit, early revenue, and happy customers — and now we’re ready to accelerate.
Our long-term vision is to become the primary destination for accessing high-quality web data. Try the product at sumble.com.
Our Team: We are a team of 15, including 10 engineers with experience at companies such as Google, Meta, Stack Overflow, and Kaggle.
What you'll do
- Finetuning small language models
- Improving the quality of existing data using scalable approaches. Examples include: making sure URLs are associated the right company, we have the correct HQ address, we have mapped parents-subsidiary using techniques like LLM validation, SERP, and triangulating across sources.
- Adding new signals: this usually involves scrubbing, matching and normalizing new signals and matching to our existing ontology
- Pushing solutions into production environments, which may involve touching data pipelines and/or backend systems
Apply on company website Remote Remote (Global) Full-time
Sumble is hiring a Data Scientist/Machine Learning Engineer
About company
Sumble gives GTM teams a structured view of each target account, including teams, reporting lines, technologies, hiring activity, and buying signals, so reps know who to reach, why now, and how to personalize outreach.
It provides full account intelligence by mapping teams, technologies, and active initiatives inside organizations. This helps sales and growth teams focus on the right people with the right message based on real-time context.
Sumble is built for sales, growth, RevOps, and GTM teams that sell into technical or complex accounts and need better account research, prioritization, and outbound context.