Lead Machine Learning Engineer

Responsibilities

• Collaborate with Product Managers, Data Scientists, Data Engineers, Software Engineers, and Business teams to develop data and machine learning products.
• Own objectives and key results for the designated workstream and partner with your manager on technical solutions.
• Architect and deliver robust systems to train, deploy, run inference, and monitor ML and AI applications at scale.
• Champion code quality, reusability, scalability, maintainability, and security; contribute to strategic architecture discussions.
• Establish processes and tools to ensure data quality and enforce data governance and engineering best practices.
• Integrate ML and AI components with production applications.
• Explore innovative approaches and stay informed on current research and technologies within the ML engineering community.

Requirements

• Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or another quantitative field.
• 7+ years of experience as an engineer focused on building machine learning systems.
• 2+ years of technical leadership delivering ML solutions in collaboration with engineers, scientists, and business stakeholders.
• Strong Python programming skills and a solid foundation in core computer science concepts.
• Experience with ML and AI frameworks and libraries such as scikit-learn, HuggingFace, PyTorch, TensorFlow/Keras, and MLlib.
• Ability to design, train, and evaluate ML models following best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, and dimensionality reduction.
• Experience with MLOps practices such as automated model deployment, model performance monitoring, and data drift detection.
• Experience building batch and streaming data pipelines using SQL, PySpark, Pandas, and related tools.
• Familiarity with data warehouses (dimensional modeling), data lakes or lakehouses, and other data architectures.
• Experience orchestrating complex workflows and data pipelines using Airflow or similar tools.
• Ability to load test deployed models at scale to identify performance bottlenecks.
• Proficiency with Git, CI/CD pipelines, Docker, and Kubernetes.
• Experience designing solutions on AWS or comparable public cloud platforms.
• Experience developing data APIs, microservices, and event-driven architectures to integrate ML systems.
• Familiarity with Large Language Models, generative AI, and their production applications.
• Experience assessing and adopting new data tools to enhance the ML stack.
• Strong interpersonal and verbal communication skills, with a track record of technical leadership and mentorship.

Technologies

• Python
• scikit-learn
• HuggingFace
• PyTorch
• TensorFlow/Keras
• MLlib
• SQL
• PySpark
• Pandas
• Airflow
• Git
• Kubernetes
• Docker
• AWS
• Snowflake
• RDS
• DynamoDB
• Kafka
• Fivetran
• dbt
• EMR
• Sagemaker
• DataDog
• Data cataloging, data observability, and data governance tooling

Benefits

• Paid time off for vacation, holidays, and sick days
• Medical, dental, and vision insurance options
• 401(k) retirement plan
• Long-term incentive programs

Additional Information

The salary range reflects the base pay the company believes it will pay for this role at the time of posting, based on job grade. Final compensation may vary by geographic location and other factors, and the range may be adjusted in the future.

A comprehensive benefits package is provided to eligible employees, including paid time off, health insurance, and a 401(k) plan.

This position is eligible to participate in the companys long-term incentive programs.