Yieldmo is hiring a
VP Machine Learning Operations

Summary

Join Yieldmo, an advertising technology company, as an experienced architect and engineering leader to spearhead machine learning operations. Design and implement systems to support data science initiatives in a low latency high volume production environment.

Requirements

• 5+ years of experience with AWS or GCP
• 10+ years of experience in software development with Python in a production environment focused on ETL, ML, and pipeline work
• 10+ years of experience working with large datasets (working with raw logs in the order of 200 - 250TB a day. Comfortable working with databases with tables ranging from 100's of billions to trillions of rows.)
• 5+ years of experience working as a data scientist with technologies such as scikit-learn, R, SQL, H2O, Vertex AI, AWS Sagemaker, TensorFlow, PyTorch, and Spark
• Comfortable building machine learning models on sparse datasets with high dimensionality
• 5+ years with predictive analytics and developing optimization algorithms
• Deep understanding of development and deployment processes with technologies including GitHub actions, containerization using Docker, and cloud services such as ECR/ECS, Lambda, and Cloudwatch
• Experience working with Airflow or another workflow orchestration tool
• Expert-level understanding of SQL in Snowflake and MySQL environments
• Experience with high-performance SQL-based analytics data warehouses such as Snowflake, BigQuery, Redshift, Vertica, or Netezza
• MS or equivalent combination of education and experience in Computer Science, Engineering, Information Systems, or quantitative science (Physics, Math, Computational Biology, Operations Research, etc.)
• Strong verbal and written communication skills
• Ad tech experience (SSPs, DSPs, Analytics, DMPs, CDPs)

Responsibilities

• Architect, deploy, and manage a software framework to securely handle data science workflows not limited to machine learning in production-grade environments
• Facilitate seamless integration of ML models into operational pipelines between engineering and data science
• Design, implement, and maintain scalable, efficient ML pipelines on AWS and GCP
• Design, implement, and maintain CI/CD pipelines to automate the continuous integration and delivery of ML models
• Monitor the performance of ML models and implement improvements
• Ensure high availability and fault tolerance of the ML infrastructure
• Optimize the performance and cost-efficiency of ML systems
• Perform code reviews; maintain and champion ML ops and engineering best practices
• Manage a team of data / ML ops engineers
• Recruit, mentor, and grow machine learning engineering team

Benefits

• Fully remote workplace
• Generous employer contribution to Health Benefit premiums & 401k Match
• Work/life balance: flexible PTO, competitive compensation packages, Summer Fridays & much more
• 1 Mental Escape (ME) day each quarter to fully unplug and recharge
• A generous learning stipend and other opportunities for professional development
• An allowance to help you upgrade your home office