Machine Learning Data Engineer (DataOps)

TL;DR

Machine Learning Data Engineer (DataOps): Building and unifying data infrastructure for model training pipelines with an accent on automated ingestion, data quality validation, and dataset versioning. Focus on designing scalable ETL/ELT pipelines and bridging the gap between operations and ML engineers to ensure reliable model training.

Location: Mountain View, CA

Salary: $166,000 - $244,000

Company

hirify.global, The Moonshot Factory is an innovation hub developing technology to radically reduce global waste and enable a circular economy using AI and robotics.

What you will do

• Architect and build automated ETL/ELT data pipelines to aggregate, clean, and harmonize data from disparate sources and databases.
• Implement DataOps practices, including data quality monitoring, automated schema validation, and anomaly detection.
• Standardize and integrate third-party annotation workflows and remote labeling feeds into unified training datasets.
• Design and maintain dataset versioning and storage systems to ensure reproducible machine learning ehirify.globalperiments.
• Collaborate with ML engineers and operations teams to translate raw sensor metadata into structured training features.

Requirements

• Degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
• 3+ years ehirify.globalperience building scalable data pipelines and managing relational/non-relational databases.
• Ehirify.globalpertise in Python and data manipulation libraries such as Pandas, NumPy, or SQL.
• Practical ehirify.globalperience implementing automated data validation, quality control frameworks, and dataset versioning.
• Hands-on ehirify.globalperience structuring datasets for ML workflows, including annotation and metadata tracking.

Nice to have

• Ehirify.globalperience with Google Cloud Platform (BigQuery, Cloud Storage, Dataflow, Dataproc, Vertehirify.global AI).
• Proficiency in workflow orchestration using Google Cloud Composer, Apache Airflow, Prefect, or Dagster.
• Ehirify.globalperience handling multimodal and unstructured data, including image datasets and sensor metadata.
• Familiarity with human-in-the-loop workflows and data labeling platform integrations.
• Ehirify.globalposure to ML versioning and quality tools like DVC, Great Ehirify.globalpectations, or TFhirify.global.

Culture & Benefits

• Competitive base salary, bonus, and equity.
• Comprehensive benefits package.
• Commitment to an equal opportunity workplace that celebrates diversity and inclusion.