Machine Learning Engineer II

Jobtailor; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor in the United States seeks an experienced ML Engineer to build batch and real-time pipelines for ad delivery, ranking, bidding, and optimization. You will deploy models into low-latency systems and collaborate with product, engineering, and analytics teams.

You will design data pipelines for large-scale impression, click, and conversion data, create reusable components and APIs, and own ML services within cross-functional teams.

Qualifications

• 2+ years of industry experience in machine learning engineering, including model development and production deployment.
• Bachelor's degree in a related technical field or equivalent practical experience.
• Proficiency in Python or Java and experience with ML frameworks.

Responsibilities

• Build batch and real-time ML pipelines for ad delivery, ranking, bidding, and optimization.
• Deploy and integrate ML models into low-latency ad delivery and campaign systems.
• Develop and maintain data pipelines for large-scale impression, click, and conversion data.
• Create reusable ML components, APIs, and workflows to support experimentation and iteration.

Skills

Python or Java Proficiency

Real-time ML systems

Software engineering fundamentals

ML model deployment

API design

Education

BS in CS/Engineering

Tools

Spark

Databricks

AWS

Job description

• Build batch and real-time ML pipelines for ad delivery, ranking, bidding, and optimization
• Deploy and integrate ML models into low-latency ad delivery and campaign systems
• Develop and maintain data pipelines for large-scale impression, click, and conversion data
• Create reusable ML components, APIs, and workflows to support experimentation and iteration
• Ensure reliability, scalability, and performance through monitoring and continuous optimization
• Analyze data to improve models, features, and business outcomes
• Collaborate with product, engineering, and analytics teams to deliver marketplace impact
• Apply software engineering best practices and support responsible use of AI/ML
• Own and deliver ML components or services within cross-functional teams
• Mentor others and help set technical direction and engineering standards

Requirements

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience
• 2+ years of industry experience in machine learning engineering, including model development and production deployment
• Proficiency in Python or Java
• Experience using machine learning frameworks
• Solid software engineering fundamentals
• Proven ability to own and deliver ML components or services within cross-functional teams
• Familiarity with real-time or near-real-time ML systems and production operations
• Experience operating production ML systems at scale, including monitoring, troubleshooting, and iterative improvement
• Familiarity with distributed data processing, including Spark and Databricks
• Familiarity with cloud platforms, preferably AWS
• Exposure to MLOps practices, including model deployment, monitoring, retraining, and CI/CD for ML
• Experience building or experimenting with ranking, prediction, classification, recommendation, or NLP models
• Experience designing APIs, data pipelines, and data models for ML-powered applications
• Interest or background in advertising, marketplaces, e-commerce, or travel platforms
• Awareness of responsible and secure AI/ML practices in production environments
• Employees must be able to work in the office at least three days a week
• No relocation assistance is available

Core Competencies

Demonstrates expertise in building and deploying machine learning pipelines, with a strong foundation in software engineering principles and experience in real-time ML systems. Capable of collaborating across teams to enhance model performance and business outcomes while adhering to responsible AI/ML practices.

Highest-signal resume keywords

• Machine Learning Engineering
• Python or Java Proficiency
• MLOps Practices
• Distributed Data Processing
• Cloud Platforms (AWS)

ATS Optimization Keywords

Hard Skills

• Machine Learning Frameworks
• Data Pipeline Development
• API Design
• Model Deployment
• Monitoring and Troubleshooting
• Ranking Models
• Prediction Models
• Classification Models
• Recommendation Models
• Natural Language Processing (NLP)

Soft Skills

• Collaboration
• Mentoring
• Technical Direction

Industry Keywords

• Advertising
• Marketplaces
• E-commerce
• Travel Platforms

Tools & Technologies

• Spark
• Databricks
• AWS