Machine Learning Engineer
$109,500–$208,500 year
RemoteUnited States
Job Summary
Own small to medium machine learning system components from technical design through implementation and delivery. Translate requirements into maintainable code, build data pipelines and feature engineering workflows, and design, train, and evaluate models with minimal supervision. Implement solutions as microservices, APIs, batch jobs, or streaming components while supporting production monitoring for metrics, data drift, and retraining triggers. Collaborate with data engineers, software engineers, product partners, and stakeholders to deliver objectives, documenting technical decisions for diverse audiences. This role supports Allergan Aesthetics' portfolio of aesthetics brands and products, including facial injectables and skin care, within a remote environment.
Required Qualifications
- Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field
- 3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python
- Strong programming skills in Python and solid understanding of core computer science principles
- Experience with data manipulation frameworks such as Pandas and PySpark
- Experience with machine learning libraries such as scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib
- Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection
- Working knowledge of SQL and relational data structures
- Ability to design, train, and evaluate machine learning models using standard best practices such as model selection, validation, bias/variance tradeoffs, and performance assessment
- Familiarity with batch and streaming data pipeline concepts such as ETL, ELT, and stream processing
- Experience working with cloud environments, preferably AWS
- Familiarity with technologies such as APIs, microservices, Docker, and Kubernetes
- Strong interpersonal, verbal, and written communication skills
- Ability to work effectively in a remote environment using collaboration tools
Desired Qualifications
- Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
- Experience with managing and architecting solutions on AWS
- Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
- Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Data Cataloging tools, Data Observability tools and Data Governance tools
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.