Lead ML Engineer, Performance Marketing

This is a fully remote position, open to applicants in United States.

📋 Description

• Lead the design and implementation of production machine learning systems that enhance performance marketing optimization across various channels.

• Architect machine learning solutions that integrate multiple models, optimization algorithms, and embedded business logic.

• Speed up the transition from research to production by utilizing scalable infrastructure, reusable tools, and enhanced machine learning development practices.

• Collaborate with data scientists to convert new models and optimization strategies into scalable production solutions.

• Design and manage real-time machine learning functionalities that operate reliably under production latency constraints.

• Develop monitoring and observability for interconnected machine learning systems to facilitate quick issue identification and resolution.

• Set technical standards for maintainable and reliable machine learning systems while enhancing team development and operations.

• Mentor data scientists and analysts on machine learning systems and production engineering.

• Oversee the technical direction throughout the machine learning lifecycle, from architecture and implementation to deployment and operation.

• Collaborate with data scientists, engineering teams, and business stakeholders on production machine learning systems aimed at performance marketing.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.

• Over 5 years of experience in designing, developing, deploying, and maintaining machine learning systems and model pipelines in collaboration with data scientists.

• Proficient in Python and foundational software engineering principles.

• Experience in building and managing production machine learning systems, including real-time model serving, deployment, monitoring, debugging, and workflow orchestration.

• Capability to design reproducible systems with clear lineage, versioning, and operational visibility across intricate machine learning workflows.

• Comfort with complex machine learning systems that involve multiple models, optimization or search algorithms, and integrated business logic.

• Strong judgment concerning model evaluation, code quality, system reliability, and maintainable engineering trade-offs.

• Working knowledge of experimentation and statistical model evaluation within production machine learning environments.

• Experience with cloud-based machine learning infrastructure and data platforms such as AWS, GCP, or Azure.

• Familiarity with infrastructure as code tools, such as Terraform.

• Excellent communication skills with the ability to convey technical trade-offs to both technical and non-technical audiences.

• On-camera participation is mandatory for virtual interviews.

• Preferred: Master’s or PhD in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.

• Preferred: Familiarity with performance marketing systems, including auction-based advertising, algorithmic bidding, or bid optimization models.

• Preferred: Experience with machine learning and data tooling, orchestrators, and platforms such as MLflow, Metaflow, Airflow, Dagster, Snowflake, Databricks, dbt, and Spark.

• Preferred: Experience in building shared machine learning infrastructure, developer tools, or reusable systems that enhance data science productivity.

• Preferred: Proficiency in using generative AI and agentic tools to streamline the end-to-end machine learning development process.

🏝️ Benefits

• Eligible for bonuses and long-term incentives.

• Flexibility to work from the location that suits you best within the U.S.

• Reasonable accommodations provided throughout all phases of the hiring process.