ML Data Engineer - DataOps & Pipelines

X Development, LLC; Mountain View (CA); On-site; USD 166,000 - 244,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

X is seeking a Machine Learning Data Engineer (DataOps) in Mountain View to build unified data infrastructure for model training. You will design automated ingestion pipelines, establish data quality validation, and manage dataset versioning to support scalable ML workflows.

You will bridge operations, remote annotation teams, and ML engineers to ensure models train on reliable, well-structured data, with a focus on data pipelines, quality controls, and feature engineering.

Qualifications

• Degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
• 3+ years experience building scalable data pipelines and unifying fragmented data storage systems.
• Proficiency in Python and data manipulation libraries (Pandas, NumPy, SQL).
• Experience implementing automated data validation, quality control, and dataset versioning.
• Hands-on experience structuring datasets for machine learning workflows.

Responsibilities

• Architect and build automated ETL/ELT data pipelines to aggregate data from disparate sources.
• Implement DataOps practices, data quality monitoring, and schema validation.
• Standardize and integrate third-party annotation workflows into unified datasets for model training.
• Design and maintain dataset versioning and storage systems for reproducible experiments.
• Collaborate with ML engineers to translate raw data into structured training features.

Skills

Data pipelines

Python

Data quality

ML data lifecycle

Data architecture

Education

Bachelor's degree in CS/related field

Tools

BigQuery

Cloud Storage

Dataflow

Dataproc

Vertex AI Data Pipelines

Job description

X is seeking a Machine Learning Data Engineer (DataOps) in Mountain View to build unified data infrastructure for model training. You will design automated ingestion pipelines, establish data quality validation, and manage dataset versioning to support scalable ML workflows.

You will bridge operations, remote annotation teams, and ML engineers to ensure models train on reliable, well-structured data, with a focus on data pipelines, quality controls, and feature engineering.