Lead Machine Learning Engineer

Allergan Aesthetics•San Diego, CA

19h

About The Position

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.

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

Nice To Haves

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

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