Principal Machine Learning Engineer

What you'll do

• Define and own the end-to-end architecture for the N&E ML platform across multiple brands.
• Author architecture documents and oversee implementation to ensure scalable and reliable solutions.
• Identify and prioritize high-impact ML workstreams while managing risks and sequencing complex initiatives.
• Design and evolve infrastructure for data pipelines, feature stores, batch training, and low-latency online serving.
• Champion the adoption of new technologies like LLMs, RAGs, and agentic AI workflows.
• Own production incident response and drive a culture of reliability and observability.
• Lead metrics-driven programs that connect ML platform investments to measurable business outcomes.
• Mentor senior engineers and foster a culture of technical rigor and continuous learning.

What we're looking for

• Bachelor’s degree in computer science, Information Systems, Statistics, Math, or a comparable field of study, and/or equivalent work experience.
• 10+ years of experience building and operating ML engineering systems in production environments.
• Deep expertise in data science, deep learning algorithms, and statistical methods applied to large-scale engineering problems.
• Demonstrated experience owning architecture across a significant platform or product domain, including authoring documents and leading implementation.
• Proven ability to drive quantifiable improvements in ML platform capabilities, personalization quality, or recommendation system performance.
• Experience designing and evolving backend microservices for large-scale distributed systems using REST.
• Strong expertise with cloud infrastructure, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize).
• Deep hands-on experience with big data technologies such as Databricks, Spark, Kinesis, and Kafka.
• Experience leading incident response for high priority incidents and driving reliability programs across a team or platform.
• Experience working in Agile/Scrum environments with strong prioritization and stakeholder management skills.
• Experience with agentic AI workflows and frameworks (e.g. LangGraph, AutoGen, CrewAI) (preferred).
• Familiarity with AI-assisted development tools such as Claude, Cursor, or GitHub Copilot (preferred).
• Familiarity with prompt engineering, fine-tuning, and evaluation frameworks for large language models in production environments (preferred).
• Experience with MLOps platforms and modern model lifecycle management tools (e.g. MLflow, SageMaker, Vertex AI) (preferred).