Machine Learning Engineer

AbbVie

. Own small to medium components of machine learning systems from technical design through implementation and delivery .

Posted 10/8/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $109,500 - $208,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building, evaluating, and deploying machine learning models and pipelines using Python, with a strong understanding of MLOps practices and cloud environments, particularly AWS. Capable of collaborating effectively with cross-functional teams and communicating technical concepts to diverse audiences.

Highest-signal resume keywords

Machine Learning Pipeline DevelopmentPython ProgrammingMLOps PracticesAWS Solutions ArchitectureData Pipeline Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonPandasPySparkScikit-learnTensorFlowKerasPyTorchSQLETL

Soft Skills

Interpersonal CommunicationVerbal CommunicationWritten Communication

Tools & Technologies

DockerKubernetesAWSAirflowDatadogPagerDutyFivetranKafkaSnowflakeRDS

Industry Keywords

Data GovernanceData ObservabilityFeature EngineeringRecommender SystemsFraud Detection

Tech Stack

Tools & technologies

AirflowAWSCloudDockerDynamoDBETLKafkaKerasKubernetesMicroservicesPandasPySparkPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Own small to medium components of machine learning systems from technical design through implementation and delivery
Translate technical requirements into maintainable code and deliver workstreams according to plan
Build and maintain data pipelines and feature engineering workflows for machine learning and AI solutions
Design, train, evaluate, and refine machine learning models
Implement ML solutions for production deployment as microservices, APIs, batch jobs, or streaming components
Support production monitoring by defining and implementing metrics for model performance, data drift, anomalies, and retraining triggers
Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders
Contribute to implementation decisions and technical tradeoffs using system design, data models, and technical artifacts
Follow governance, documentation, coding, and source control standards
Support teammates with day-to-day responsibilities
Document and communicate work progress, technical decisions, and outcomes to technical and non-technical audiences

Requirements

What you’ll need

Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or another quantitative field
3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python
Strong Python programming skills and understanding of core computer science principles
Experience with Pandas and PySpark
Experience with scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib
Experience with MLOps practices, including automated model deployment, model performance monitoring, and data drift detection
Working knowledge of SQL and relational data structures
Ability to design, train, and evaluate machine learning models using standard best practices
Familiarity with ETL, ELT, and stream processing
Experience with cloud environments, preferably AWS
Familiarity with APIs, microservices, Docker, and Kubernetes
Strong interpersonal, verbal, and written communication skills
Ability to work effectively in a remote environment using collaboration tools
Knowledge of recommender systems, fraud detection, personalization, and marketing science preferred
Experience managing and architecting AWS solutions preferred
Familiarity with LLMs, generative AI modalities, and production applications preferred
Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, SageMaker, Datadog, PagerDuty, data cataloging, data observability, and data governance tools preferred

Benefits

Comp & perks

Paid time off (vacation, holidays, sick)
Medical, dental, and vision insurance
401(k)
Long-term incentive program eligibility
Remote work
Travel opportunity (10% of the time)