Lead Machine Learning Engineer

Allergan Aesthetics, an AbbVie company, develops and markets a portfolio of leading aesthetics brands and products. They are seeking a Lead Machine Learning Engineer to collaborate with cross-functional partners to build data and Machine Learning products, architect and build robust systems, and innovate with new approaches in the ML engineering community.

Responsibilities:

• Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning products
• Take ownership of objectives and key results for your workstream, and own technical solutions in partnership with your manager
• Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale
• Champion code quality, reusability, scalability, maintainability, and security, and provide input into strategic architecture decisions
• Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices
• Integrate Machine Learning and AI systems with production applications
• Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community

Requirements:

• Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field
• 7+ years of experience as an engineer specialized building Machine Learning systems
• 2+ years of technical leadership delivering machine learning solutions in partnership with engineers, scientists, and business stakeholders
• Strong programming skills in Python and understanding of core computer science principles
• Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc
• Ability to design, train, and evaluate machine learning and AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc
• Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection, etc
• Experience with building batch and streaming pipelines using complex SQL, PySpark, Pandas, and similar frameworks
• Experience with data warehouses (e.g., dimensional modeling), data lakes/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
• Experience with Git, CI/CD pipelines, Docker, Kubernetes
• Experience with architecting solutions on AWS or equivalent public cloud platforms
• Experience with developing data APIs, Microservices and event driven systems to integrate ML systems
• Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
• Experience in assessing and implementing new data tools to enhance the machine learning stack
• Strong interpersonal and verbal communication skills
• Technical leadership experience and the ability to mentor and guide others
• Knowledge of data mesh concepts
• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
• Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information
• Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, DataCataloging tools, Data Observability tools and Data Governance tools