Research Engineer, Scalable ML Data Pipelines

datologyai; San Mateo; On-site; PHP 11,314,000 - 18,856,000; Full time

Benefits offered by this job

100% health benefits

401(k) plan with 4% match

Unlimited PTO

Paid Parental Leave

Wellness stipend

Learning & development stipend

Daily lunches in office

Relocation assistance

Job summary

DatologyAI is hiring a Research Engineer to advance cutting-edge data curation and translate research into our core product pipeline. You will collaborate with researchers and engineers to scale pipelines for massive datasets and ensure production-ready systems.

The role emphasizes Python, PyTorch, and distributed compute, with opportunities to work on GPU clusters in a cloud environment. Competitive compensation and equity are offered.

Qualifications

• 3+ years building ML systems, data infrastructure, or large-scale distributed applications.
• Fluency in Python and hands-on experience with 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.

Responsibilities

• Build and scale data processing and curation pipelines over large language, vision, and multimodal datasets.
• Design experimentation infrastructure to enable rapid, scalable research iterations.
• Harden research results into production-grade data systems used by customers.
• Profile and optimize large-scale training and data workloads for performance and cost.

Skills

Python fluency

PyTorch experience

Distributed systems

Software engineering fundamentals

Tools

PyTorch

Spark

Ray

Dask

Snowflake

Job description

DatologyAI is hiring a Research Engineer to advance cutting-edge data curation and translate research into our core product pipeline. You will collaborate with researchers and engineers to scale pipelines for massive datasets and ensure production-ready systems.

The role emphasizes Python, PyTorch, and distributed compute, with opportunities to work on GPU clusters in a cloud environment. Competitive compensation and equity are offered.