Data Scientist/Machine Learning Engineer
Key Skills for This Role
Full Job Posting
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
- 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
- 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
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
Responsibilities
- 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
About Sumble
AI-powered account intelligence software helping enterprise sales and go-to-market teams understand target companies.
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