Operations Machine Learning Engineer

Ladders; United States; Remote; USD 140,000 - 163,000; Full time

Benefits offered by this job

Fully remote

Bonuses & stock grants

Comprehensive benefits

Flexible hours

Job summary

Ladders is seeking an Operations Machine Learning Engineer to join a leader in Consumer Technology. This role focuses on building the operational backbone to move ML from development into reliable production, partnering with data science and cross-functional teams to create scalable workflows and strengthen monitoring and validation practices.

Location: Remote - US based candidates only. Compensation: $140,000–$163,000 annually.

Qualifications

• Bachelor's degree in a quantitative or technical field.
• 3+ years in ML engineering, MLOps, or similar roles.
• Proficient in production-grade Python data workflows.
• Cloud tech knowledge, especially AWS and Databricks.
• CI/CD practices and deployment automation experience.
• Data pipelines and feature engineering familiarity.
• Excellent communication to translate concepts to non-technical stakeholders.

Responsibilities

• Partner with Operations Data Science to convert ML models into scalable workflows.
• Design and maintain automated data and model pipelines using Python and cloud platforms.
• Develop reusable deployment patterns and documentation for data scientists.
• Collaborate with cross-functional teams to align data science solutions with enterprise practices.
• Create monitoring frameworks for model performance and data quality.
• Support responsible AI practices, including documentation and validation workflows.
• Identify and resolve issues in ML pipelines and workflows.

Skills

Python

CI/CD

Data pipelines

Feature engineering

Communication

Education

Bachelor's degree in a quantitative or technical field

Tools

AWS

Databricks

Job description

For our client, we are seeking a Operations Machine Learning Engineer to join the team of a leader in the Consumer Technology space.

This role will focus on building the operational backbone that helps machine learning solutions move from development into reliable production use. You will partner closely with data science and cross-functional teams to create scalable workflows, improve deployment consistency, and strengthen monitoring and validation practices. The work will support enterprise standards for model performance, data quality, and responsible AI across the organization.

Location: Remote - US based candidates only, no visa sponsorship available

Compensation: $140,000 – $163,000 annually

Responsibilities

• Partner with Operations Data Science to convert machine learning models into scalable workflows
• Design and maintain automated data and model pipelines using Python and cloud platforms
• Develop reusable deployment patterns and documentation for data scientists
• Collaborate with cross-functional teams to align data science solutions with enterprise practices
• Create monitoring frameworks for model performance and data quality
• Support responsible AI practices, including documentation and validation workflows
• Identify and resolve issues in machine learning pipelines and workflows

Qualifications

• Bachelor's degree in a quantitative or technical field such as computer science or data engineering
• 3+ years of experience in machine learning engineering, MLOps, or similar roles
• Proficient in building production-grade data workflows using Python
• Strong knowledge of cloud technologies, especially AWS and Databricks
• Experience with CI/CD practices and deployment automation
• Familiarity with data engineering concepts like data pipelines and feature engineering
• Excellent communication skills for translating technical concepts to non-technical stakeholders

Benefits

• Fully remote work arrangement with no in-office requirements
• Opportunity for quarterly bonuses and stock grants based on performance
• Access to a comprehensive range of benefits
• Flexible work hours with designated availability

Our client is an equal opportunity employer.