Data Scientist/Machine Learning Engineer
At a glance
Mid-level AI Research role at Sumble Inc. Remote, United States · no salary stated · we estimate $135,000–$232,000 across the United States.
Sumble Inc has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Fine-tune compact language models for the role’s data tasks
- Use scalable checks to correct company URLs, headquarters, and parent-subsidiary relationships across sources
- Clean and align incoming signals with the existing ontology before adding them
- Move completed approaches into production, sometimes changing data pipelines or backend services
What you bring
- You’re located in a timezone within the Americas
- You can work across machine-learning tasks and production systems, including data pipelines or backend services
Who this fits
This role suits someone comfortable moving between model tuning, data quality work, and production implementation. You need to be based in an Americas timezone. The position is listed as mid-level and remote within the United States.
From the employer
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
Requirements
- Located within Americas timezones
More about Sumble
Our Tech Stack:
- ML/Data: PyTorch, Huggingface, Gemma models, LORA, VLLM, Skypilot, Marimo
- Languages & Frameworks: Python, FastAPI, React, Typescript
- Cloud Platform: Google Cloud Platform (GCP)
- Databases: PostgreSQL, DuckDB
- Infrastructure: Cloud Run
- Product/Design: Figma, Vercel V0
Challenges We Tackle:
- Transforming noisy datasets into high-quality data products
- Running expensive analytics computations efficiently
- Managing the complexity of a growing number of data sources, machine learning models, and large data operations
- Create a great PLG experience with upsell pathways
Benefits
- Medical, dental, and vision (US)
- 401k (US)
- Target 4 weeks PTO