VP, Machine Learning Operations

Who We Are

Yieldmo is an advertising platform that helps brands invent creative experiences through tech and AI, using custom ad formats, proprietary attention signals, predictive format selection, and privacy-safe premium inventory curation. Yieldmo believes all ads should be human-centered, tailored, and provoke users' emotions and actions. Yieldmo helps brands deliver the best ad for every impression opportunity, merging creative and media for proven results.

What We Need

We’re looking for an experienced architect and engineering leader to spearhead our machine learning operations, with a focus on optimizing the deployment and management of ML models. You will design and implement systems to support our data science initiatives in a low latency high volume (500B - 1T transactions a day) production environment. You are experienced with the lifecycle of machine learning, data science, data and ML pipeline design, and have a deep understanding of how they can be deployed and orchestrated in a cloud-based infrastructure. Ideally, you would have worked as a data scientist for some years and decided your passion lies in the ML ops / data engineering side of data science. We currently have multiple ML implementations that we’d like to unify into a common framework to ensure security, support reusability and observability, and expedite the ability to make changes. You should be hands-on and capable of implementation in addition to design. You will partner with our data scientists to set best practices, automate data science approaches, architect solutions, and deploy and monitor production workloads.

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
• Contribute to and evolve our current data science initiatives, which include cookieless targeting, format optimization, creative optimization, traffic throttling, and real-time bidding management

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)

Nice to Haves

• Experience working with Jinja templates
• Exposure to A/B testing
• Exposure to Generative AI interfaces and infrastructure
• Comfortable reading code in Java

What We Offer

We believe that diverse people and perspectives lead to breakthrough ideas, therefore we provide comprehensive benefits and an inclusive culture to support our valued team members.

• Remote Work: Our team is fully distributed, though we love an opportunity to get together at our annual offsites, holiday parties, and more.
• 100% Company Paid Health Coverage: Choose the medical, dental, and vision plan that’s best for you and your family – all with options for 100% company paid coverage.
• 401(k) Plan: Invest in yourself by participating in our 401(k) plan with a company match.
• Equity:  Share in Yieldmo’s success through our employee stock option program.
• Flexible Time Off, Company Slowdowns, and Summer Fridays: Take time off to relax and rejuvenate on your own terms with flexible time off, multiple company slowdowns, and Summer Fridays.
• Home Office Setup and Stipend: Setup your home office for success with our premium technology packages and an additional stipend for any extra needs.
• Professional Development:  Grow your hard and soft skills with our annual professional development stipend.

US Jobs: The base salary range for this role is: $250,000-$300,000 per year. The range listed is just one component of Yieldmo's total compensation package for employees. Individual compensation decisions are based on a number of factors, including experience, level, skillset, and balancing internal equity relative to peers at the company. We recognize that the person we hire may be less experienced (or more senior) than this job description as posted. In these situations, the updated salary range will be communicated with you as a candidate. For all other countries, we have competitive pay bands based on market standards.