Machine Learning Engineer

This is an Individual Contributor role in content recommendations. You will be expected to lead

recommendation and personalization algorithm research, development, and optimization for product

areas, and to coordinate requirements and manage stakeholder expectations with Product,

Engineering, and Editorial teams. You will be expected to help meet KPIs for product areas and to

set and meet deadlines for external and internally facing tools, such as offline evaluation tools for

pre-production algorithms. As an IC, you will also be responsible for helping to set the roadmap for

algorithmic work — not only for how to approach product requests for new recommendation

features, but for helping to drive larger company objectives in the areas of personalization and

content recommendation.

• Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop algorithms for personalization, recommendation, and other predictive systems; maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams
• Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets
• Development Best Practices: Maintain existing and establish new algorithm development, testing, and deployment standards
• Collaborate with product and business stakeholders: Identify and define new personalization opportunities and work with other data teams to improve how we do data collection, experimentation and analysis

Required Qualifications:

• 7+ years of analytical and/or data engineering experience
• 7+ years of experience developing machine learning models and performing data analysis with Python or R
• 5+ years writing production-level, scalable code (e.g. Python, Scala)
• 5+ years of experience developing algorithms for deployment to production systems
• In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings
• Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark
• Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate
• Strong written and verbal communication skills

Additional Preferred Qualifications:

• MS or PhD in statistics, math, computer science, or related quantitative field
• Production experience with developing content recommendation algorithms at scale
• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment
• Familiar with metadata management, data lineage, and principles of data governance
• Experience loading and querying cloud-hosted databases such as Snowflake
• Building streaming data pipelines using Kafka, Spark, or Flink

Required Education: BS CS Math Stats