Machine Learning Engineer
New
A
AbbVieMachine learning
United StatesFull-TimeMiddle
Salary109,500 - 208,500 USD per year
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Job Details
- Experience
- 3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python
- Required Skills
- AWSPythonSQLMachine LearningPyTorchPandasTensorflowscikit-learnMLOpsPySpark
Requirements
- Hold a BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or another quantitative field.
- Have 3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python.
- Demonstrate strong Python programming skills and a solid understanding of core computer science principles.
- Have experience with data manipulation frameworks such as Pandas and PySpark.
- Have experience with machine learning libraries such as scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib.
- Have experience with MLOps practices, including automated model deployment, performance monitoring, and data drift detection.
- Have working knowledge of SQL and relational data structures.
- Be able to design, train, and evaluate models using model selection, validation, bias/variance tradeoffs, and performance assessment.
- Be familiar with batch and streaming data pipeline concepts, including ETL, ELT, and stream processing.
- Have experience working with cloud environments, preferably AWS.
- Be familiar with APIs, microservices, Docker, and Kubernetes.
- Be able to work effectively in a remote environment using collaboration tools.
Responsibilities
- Own small to medium machine learning system components from technical design through implementation and delivery.
- Translate technical requirements into maintainable code and deliver planned workstreams.
- Build and maintain data pipelines and feature engineering workflows for machine learning and AI solutions.
- Design, train, evaluate, and refine machine learning models using sound statistical and engineering practices.
- Implement production-ready ML solutions as microservices, APIs, batch jobs, or streaming components.
- Help define and implement monitoring 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 tradeoffs based on system design, data models, and technical artifacts.
- Document and communicate work progress, technical decisions, and outcomes to technical and non-technical audiences.
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