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 DescriptionResponsibilities:
- 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
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 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
Preferred Experience & Skills:
- 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
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 is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of thisposting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location,and we may ultimately pay more or less than the posted range. This range may be modified in the future.
We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
This job is eligible to participate in our long-term incentive programs.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission,incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless anduntil paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.
AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.
US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html
US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:
https://www.abbvie.com/join-us/reasonable-accommodations.html
Skills Required
- 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 with engineers, scientists, and business stakeholders
- Strong programming skills in Python and understanding of core computer science principles
- Experience with ML/AI frameworks and libraries: scikit-learn, HuggingFace, PyTorch, TensorFlow/Keras, MLlib
- Ability to design, train, and evaluate ML models following best practices (model selection, validation, bias/variance tuning, sensitivity analysis)
- Experience with MLOps practices: automated model deployment, performance monitoring, data drift detection
- Experience building batch and streaming pipelines using complex SQL, PySpark, and Pandas
- Experience with data warehouses, data lakes/lakehouses, and dimensional modeling/data architectures
- Experience orchestrating 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, and Kubernetes
- Experience architecting solutions on AWS or equivalent public cloud platforms
- Experience developing data APIs, microservices, and event-driven systems to integrate ML systems
- Familiarity with Large Language Models (LLMs) and other generative AI modalities in production
- Experience assessing and implementing new data tools to enhance the ML stack
- Strong interpersonal and verbal communication skills
- Technical leadership experience with ability to mentor and guide others
- Knowledge of data mesh concepts
- Knowledge in recommender systems, fraud detection, personalization, and marketing science
- Knowledge of vector databases and knowledge graphs
- Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, EMR, SageMaker, DataDog, PagerDuty, data cataloging, observability, and governance tools
AbbVie Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AbbVie and has not been reviewed or approved by AbbVie.
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Retirement Support — 401(k) contributions include a dollar-for-dollar match up to 6% plus an additional annual company contribution tied to age and service. Company-paid life insurance and other financial protections further strengthen long-term security.
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Parental & Family Support — Paid leave programs include up to 12 weeks at 100% for parental leave and separate paid caregiver leave, with eligibility after six months where noted. Adoption and surrogacy reimbursements and family-building resources add further support.
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Healthcare Strength — Health coverage begins on day one with medical, dental, vision, mental health, and prescription benefits, and preventive care covered at 100%. Options such as HSAs/FSAs and coverage for spouses/domestic partners and children up to age 26 broaden accessibility.
AbbVie Insights
What We Do
AbbVie is a global biopharmaceutical company focused on creating medicines and solutions that put impact first — for patients, communities, and our world. We aim to address complex health issues and enhance people's lives through our core therapeutic areas: immunology, oncology, neuroscience, eye care, aesthetics and other areas of unmet need.
