Data Scientist/Machine Learning Engineer

Sumble Inc.; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical, dental, and vision

401k

4 weeks PTO

Job summary

A data-focused technology company in the U.S. is seeking a candidate to finetune language models and improve existing data quality using scalable methods. The ideal candidate will manage data pipelines and backend systems while adding new signals and validating data associations. This role provides a competitive benefits package, including medical, dental, and vision coverage, along with a 401k plan and generous PTO.

Qualifications

• Experience fine-tuning small language models.
• Ability to improve data quality at scale using scalable methods.
• Experience deploying ML/data pipelines to production.
• Collaborate with cross-functional teams including data, product, and engineering.

Responsibilities

• Finetuning small language models.
• Improving the quality of existing data using scalable approaches.
• Adding new signals and matching to existing ontology.
• Pushing solutions into production environments.

Skills

Finetuning small language models

Improving data quality

Scrubbing and normalizing signals

Data pipelines

Backend systems

Tools

PyTorch

Huggingface

Google Cloud Platform

PostgreSQL

DuckDB

Python

FastAPI

React

Typescript

Job description

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

• 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

• Medical, dental, and vision (US)
• 401k (US)
• Target 4 weeks PTO