This role pays less than
82%
of similar roles. Most pay
$195,000–$254,750
— the shaded band above.
At the midpoint, this role pays about
$180k
versus about
$225k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
297 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 271 roles with salary data.
Lead Machine Learning Engineer
The Lead Machine Learning Engineer joins the Allergan Aesthetics team to collaborate with cross-functional partners, including data scientists and software engineers, to build machine learning products. This role involves architecting and building robust systems to train, deploy, run inference, and monitor AI systems at scale while ensuring code quality and data governance. The candidate will integrate ML systems into production applications and innovate using new research. Key technologies include Python, SQL, PySpark, Pandas, and frameworks like scikit-learn, HuggingFace, PyTorch, and TensorFlow/Keras. Technical requirements involve MLOps practices, building batch and streaming pipelines, and utilizing tools such as Airflow, Docker, Kubernetes, and AWS. The role also involves working with Large Language Models, vector databases, and data architectures to solve problems in areas like recommender systems, fraud detection, personalization, and marketing science.
Architect and build robust systems to train, deploy, run inference, and monitor ML and AI systems at scale.
Integrate Machine Learning and AI systems into production applications.
Implement processes and tools to ensure data quality and enforce data governance policies.
Develop data APIs, microservices, and event-driven systems to integrate machine learning models.
Build batch and streaming pipelines using SQL, PySpark, and other relevant frameworks.
Champion code quality, reusability, scalability, maintainability, and security for all technical solutions.
Evaluate and implement new data tools to enhance the existing machine learning stack.
Mentor and guide team members while providing input into strategic architecture decisions.
What we're looking for
Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or another quantitative field.
7+ years of experience as an engineer specialized in building Machine Learning systems.
2+ years of technical leadership delivering machine learning solutions with engineers, scientists, and business stakeholders.
Strong programming skills in Python and understanding of core computer science principles.
Experience with ML/AI frameworks like scikit-learn, HuggingFace, PyTorch, or TensorFlow and MLOps practices such as automated deployment and monitoring.
Experience building batch/streaming pipelines using SQL, PySpark, Pandas, and orchestrating workflows with Airflow or similar tools.
Experience with Git, CI/CD, Docker, Kubernetes, and architecting solutions on AWS or equivalent public cloud platforms.
Knowledge of vector databases, knowledge graphs, and specific technologies like Snowflake, Kafka, or SageMaker (preferred).