Lead Machine Learning Engineer

Allergan Data Labs is on a mission to transform the Allergan Aesthetics beauty business at AbbVie, one of the largest pharmaceutical companies in the world. Our iconic brands include BOTOX® Cosmetic, CoolSculpting®, JUVÉDERM® and more. The medical aesthetics business is ripe for rapid growth and disruption, and we are looking to add to our high performing team to do just that. 

Our team has successfully launched a new and innovative technology platform, Allē, which serves millions of consumers, tens of thousands of aesthetics providers and thousands of colleagues throughout the US. Since its launch in November 2020, Allē has delivered curated promotions, personalized experiences and had millions of consumers use it as part of their beauty journey. 

We’re looking to add to our team as we prepare to launch a new array of game-changing technologies on our successfully adopted platform. If you’re interested in working within a startup-oriented environment, while having the backing of a very large company, please read on.

Allergan Data Labs is a vibrant startup-minded organization with the backing of a large company. As a Lead Machine Learning Engineer, you will be responsible for collaborating with cross functional partners and applying your Machine Learning Engineering skills to deliver data-driven solutions for product teams, operations, marketing, and sales.

Responsibilities

• Take ownership for achieving objectives and key results for your team, allocate resources, oversee & own technical solutions, communicate schedule, status, and milestones

• Lead and manage a small team of Machine Learning Engineers by setting goals, supervising work, providing guidance, evaluating performance, removing barriers, cultivating career development, and promoting job satisfaction

• Collaborate with cross functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data products

• Architect and build robust systems to train, deploy, run inference, and monitor machine learning and AI models at scale

• Champion code quality, reusability, scalability, maintainability, and security as well as providing input for 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

Required Experience & Skills

• 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 leading a team of Machine Learning Engineers in projects to deliver data solutions

• 1+ year of experience as the reporting manager for a small number of individual contributors

• 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 with orchestrating complex workflows and data pipelines using like Airflow or similar tools

• Ability to load test deployed models at scale to understand performance breakpoints

• 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

• Experie