Lead Machine Learning Engineer

Allergan Aesthetics, part of the AbbVie family, invites you to join as a Lead Machine Learning Engineer in person in San Diego, CA. This role centers on designing and deploying scalable ML systems, partnering with cross-functional teams, and upholding data quality and governance. You will help shape data and ML products that impact real-world outcomes and collaborate across product management, data science, data engineering, software, and business units.

Compensation and benefits: the base salary range for this role is $124,500 to $236,500 per year, with eligibility for long-term incentive programs. Allergan Aesthetics provides a comprehensive benefits package including paid time off (vacation, holidays, sick leave), medical/dental/vision insurance, and a 401(k) plan to eligible employees. The role is onsite in San Diego, CA.

Responsibilities

• Collaborate with cross-functional partners to build data and machine learning products, including Product Managers, Data Scientists, Data Engineers, Software Engineers, and Business teams.
• Own objectives and key results for your workstream and shape technical solutions in partnership with your manager.
• Architect and implement robust systems to train, deploy, run inference, and monitor ML and AI solutions at scale.
• Champion code quality, reusability, scalability, maintainability, and security; contribute to strategic architecture decisions.
• Implement processes and tools to ensure data quality and enforce data governance and engineering best practices.
• Integrate ML and AI systems with production applications and workflows.
• Experiment with new approaches and stay current with research and advances in the ML engineering field.

Requirements

• Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field.
• 7+ years of experience building machine learning systems as an engineer.
• 2+ years of technical leadership delivering ML solutions in collaboration with engineers, scientists, and business stakeholders.
• Strong programming skills in Python and a solid grounding in core computer science principles.
• Experience with ML and AI frameworks such as scikit-learn, HuggingFace, PyTorch, TensorFlow/Keras, MLlib, and related tools.
• Ability to design, train, and evaluate ML models following best practices including model selection, validation, bias/variance considerations, performance assessment, and sensitivity analysis.
• Experience with MLOps practices such as automated model deployment, monitoring, and data drift detection.
• Experience building batch and streaming pipelines using SQL, PySpark, Pandas, and similar technologies.
• Background with data warehouses, data lakes or lakehouses, and related data architectures.
• Experience orchestrating complex workflows with Airflow or equivalent tools.
• Ability to load test deployed models at scale to identify performance bottlenecks.
• Proficiency with Git, CI/CD pipelines, Docker, Kubernetes, and cloud platforms (AWS or equivalent).
• Experience developing data APIs, microservices, and event-driven architectures to integrate ML systems.
• Familiarity with Large Language Models and generative AI in production contexts.
• Track record of evaluating and adopting new data tools to enhance the ML stack.
• Strong interpersonal and verbal communication skills; proven technical leadership and mentoring ability.

Technologies

• Python, scikit-learn, HuggingFace, PyTorch, TensorFlow/Keras, MLlib
• SQL, PySpark, Pandas
• Airflow, Git, Kubernetes, Docker
• AWS (or equivalent cloud), Snowflake, RDS, DynamoDB
• Kafka, Fivetran, dbt, EMR, SageMaker
• DataDog, PagerDuty, Data Cataloging and Data Governance tools

Benefits

• Paid time off including vacation, holidays, and sick leave
• Medical, dental, and vision insurance
• 401(k) retirement plan
• Long-term incentive programs