Lead Machine Learning Engineer

Company Description About AbbVie At Allergan Aesthetics, an AbbVie company, we develop, manufacture, and market a portfolio of leading aesthetics brands and products. Our aesthetics portfolio includes facial injectables, body contouring, plastics, skin care, and more. Our goal is to consistently provide our customers with innovation, education, exceptional service, and a commitment to excellence, all with a personal touch. For more information, visit https://global.allerganaesthetics.com/. Follow Allergan Aesthetics on LinkedIn. Job Description Responsibilities: Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning productsTake ownership of objectives and key results for your workstream, and own technical solutions in partnership with your managerArchitect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scaleChampion code quality, reusability, scalability, maintainability, and security, and provide input into strategic architecture decisionsImplement processes and tools to ensure data quality, enforce data governance policies and engineering best practicesIntegrate Machine Learning and AI systems with production applicationsInnovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community Qualifications Required Experience & Skills: Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field7+ years of experience as an engineer specialized building Machine Learning systems2+ years of technical leadership delivering machine learning solutions in partnership with engineers, scientists, and business stakeholdersStrong programming skills in Python and understanding of core computer science principlesExperience 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 frameworksExperience with data warehouses (e.g., dimensional modeling), data lakes/Lakehouses, and other data architecturesExperience orchestrating complex workflows and data pipelines using Airflow or similar toolsAbility to load test deployed models at scale to identify performance bottlenecksExperience with Git, CI/CD pipelines, Docker, KubernetesExperience with architecting solutions on AWS or equivalent public cloud platformsExperience with developing data APIs, Microservices and event driven systems to integrate ML systemsFamiliarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in productionExperience in assessing and implementing new data tools to enhance the machine learning stackStrong interpersonal and verbal communication skillsTechnical leadership experience and the ability to mentor and guide othersPreferred Experience & Skills: Knowledge of data mesh conceptsKnowledge in domains such as recommender systems, fraud detection, personalization, and marketing scienceKnowledge of vector databases, knowledge graphs, and other approaches for organizing & storing informationFamiliarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, DataCataloging tools, Data Observability tools and Data Governance tools Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: ​ The compensation range described below i