Senior Machine Learning Engineer

What you'll do

• Lead end-to-end development of production ML systems including data sourcing, feature engineering, training, deployment, and monitoring.
• Own flagship ML products such as probabilistic identity resolution, lookalike modeling, and demand forecasting.
• Design and implement scalable feature and inference pipelines using Databricks, PySpark, and Snowflake.
• Develop and optimize diverse models including gradient boosting, two-tower retrieval, neural ranking, and causal-inference techniques.
• Champion MLOps best practices by managing model versioning, automated retraining, drift detection, and production monitoring.
• Design and prototype agentic ML workflows using frameworks like LangChain or LangGraph to automate repetitive tasks.
• Mentor engineers across global teams and set technical standards for the broader ML organization.
• Translate business requirements from Product and Marketing stakeholders into executable ML roadmaps and technical designs.

What we're looking for

• 5–8 years of industry experience in ML engineering or applied data science (3+ years with a PhD).
• Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent experience).
• Deep Python expertise and strong software engineering practices for building and deploying ML at scale.
• Strong proficiency in Databricks (PySpark, Delta Lake, MLflow, Unity Catalog) and SQL/Snowflake.
• Experience with AWS ML services including SageMaker, S3, and Lambda.
• Demonstrated technical leadership in driving architectural decisions, setting standards, and mentoring other engineers.
• Strong understanding of ML model evaluation, A/B testing, and statistical/causal inference.
• Experience with recommendation systems, personalization, or identity resolution in media/streaming/ad-tech (preferred).