Lead Machine Learning Engineer

This onsite San Diego, CA role at Allergan Aesthetics (an AbbVie company) leads the design and deployment of scalable machine learning systems, collaborating across product, data, and engineering teams to ensure data quality and governance.

Responsibilities

• Partner with product managers, data scientists, data engineers, software engineers, and business teams to design data driven ML products
• Own objectives and key results for your workstream and co-create technical solutions with your manager
• Architect and implement end-to-end ML pipelines to train, deploy, run inference, and monitor models at scale
• Promote clean code, reusable components, scalability, maintainability, and security; contribute to architectural strategy
• Establish data quality processes and governance policies along with engineering best practices
• Integrate ML and AI solutions with production applications and systems
• Pursue innovative approaches and stay current with the latest ML research and industry developments

Requirements

• A degree (BS, MS, or PhD) in computer science, mathematics, statistics, data science, engineering, operations research, or other quantitative field
• Minimum 7 years of engineering experience focused on building ML systems
• At least 2 years in technical leadership delivering ML solutions with cross-functional teams
• Proficiency in Python and strong computer science fundamentals
• Experience with ML frameworks and libraries such as scikit-learn, HuggingFace, PyTorch, TensorFlow/Keras, MLlib, etc
• 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: automated model deployment, model performance monitoring, data drift detection
• Experience building batch and streaming data pipelines using SQL, PySpark, Pandas, and similar tools
• Experience with data warehouses, data lakes or lakehouses, and related data architectures
• Experience orchestrating complex workflows with Airflow or similar tools
• Ability to load test deployed models at scale to identify performance bottlenecks
• Experience with Git, CI/CD pipelines, Docker, and Kubernetes
• Experience architecting solutions on AWS or equivalent public cloud platforms
• Experience developing data APIs, microservices, and event driven systems to integrate ML components
• Familiarity with Large Language Models and other generative AI modalities and their production use
• Experience evaluating and adopting new data tools to enhance the ML stack
• Strong interpersonal and verbal communication skills
• Technical leadership experience with mentoring and guiding others

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
• PagerDuty
• Data cataloging tools
• Data observability tools
• Data governance tools

Benefits

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

Additional Information

• Base pay range: USD 124,500 to USD 236,500 per year; final pay depends on location, experience, and other factors
• The compensation range may be adjusted in the future
• This role is eligible for participation in long-term incentive programs
• Location is onsite in San Diego, CA
• A comprehensive benefits package is available to eligible employees