Hybrid ML Systems Engineer: Data Curation Pipelines

DatologyAI; California; Hybrid; USD 180,000 - 300,000; Full time

Benefits offered by this job

Health benefits

401(k) match

Unlimited PTO

Parental leave & WFh

Wellness stipend

Learning stipend

Lunches & snacks

Relocation support

Job summary

DatologyAI is seeking a Research Engineer to accelerate state-of-the-art data curation and integrate research into our core product pipeline. You will collaborate with technical staff to advance data strategies for massive language, vision, and multimodal datasets, ensuring scalable, reliable workflows.

You will work on production-grade systems, optimize compute, and enable researchers to translate findings into practical capabilities, with a focus on scale and efficiency.

Qualifications

• 3+ years building ML systems, data infrastructure, or large-scale distributed applications.
• Strong software engineering fundamentals, Python, and hands-on PyTorch.
• Experience with distributed data processing and/or distributed training (Spark, Ray, Dask, Snowflake).
• Comfort operating large-scale compute, including GPU clusters and cloud infrastructure.
• Demonstrated track record of shipping production systems and infrastructure.

Responsibilities

• Build and scale data processing and curation pipelines over large datasets; optimize for speed, reliability, and cost.
• Design experimentation infra to enable rapid, scalable iteration of ideas.
• Harden research results into production-grade systems relied upon by customers.
• Profile and optimize large-scale training and data workloads to balance performance and cost.

Skills

ML systems experience

Python

PyTorch

Distributed data processing

Cloud infrastructure

Tools

Spark

Ray

Dask

Snowflake

Job description

DatologyAI is seeking a Research Engineer to accelerate state-of-the-art data curation and integrate research into our core product pipeline. You will collaborate with technical staff to advance data strategies for massive language, vision, and multimodal datasets, ensuring scalable, reliable workflows.

You will work on production-grade systems, optimize compute, and enable researchers to translate findings into practical capabilities, with a focus on scale and efficiency.