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This listing doesn't post a salary. Most similar roles pay
$192,050–$246,459.
Based on 240 similar postings.
Employer
About Amgen
Amgen is a leading biotechnology company that discovers, develops, manufactures, and delivers innovative human therapeutics, with products addressing conditions such as anemia, bone loss, inflammation, and cancer. Industry: Biotechnology & Pharmaceuticals
Amgen currently has
76 open roles
on FindRole.
Listed pay typically runs
$136,906–$185,227
across 45 roles with salary data.
JOB TITLE: Senior Machine Learning Engineer
The Senior Machine Learning Engineer joins the team to develop scalable machine learning platforms and workflows that enable researchers to build, deploy, and manage AI/ML models. This role focuses on productionizing machine learning solutions primarily to support drug discovery and development. Key responsibilities include building data pipelines, feature engineering workflows, model training, evaluation, and deployment pipelines. The engineer will implement MLOps best practices such as CI/CD, cloud infrastructure as code, and experiment tracking. Required technical skills include Python, version control, test-driven development, and experience with AWS and Databricks. Preferred tools include MLflow, Kubeflow, Weights & Biases, Terraform, Docker, Kubernetes, Spark, scikit-learn, TensorFlow, PyTorch, LangChain, and graph databases. The work addresses the scientific and engineering challenges of designing new drugs in a life sciences context.
What does a Machine Learning Engineer earn in Remote?
Median $246638 from 41 postings across 11 companies.
Develop and deploy scalable machine learning platforms and workflows for drug discovery and development.
Build and maintain productionized MLOps solutions on cloud platforms like AWS or Databricks.
Create data pipelines, feature engineering workflows, and model training and evaluation pipelines.
Implement MLOps best practices including CI/CD, infrastructure as code, monitoring, and traceability.
Manage model registries, experiment tracking systems, and performance monitoring.
Transition experimental machine learning outcomes into production-grade enterprise solutions.
Develop training content and knowledge articles to educate scientists on model lifecycle management.
What we're looking for
Must have a Doctorate degree, or a Master’s with 2 years experience, or a Bachelor’s with 4 years experience, or an Associate’s with 8 years experience, or a GED with 10 years experience in Computer Science, IT, or a related field.
Must have software programming skills in Python, version control, and test-driven development.
Must have hands-on experience with AI/ML model training and serving.
Must have experience with cloud technologies, specifically Databricks and AWS (preferred).
Experience with deployment and maintenance of AI/ML models in production (preferred).
Experience with AI and MLOps frameworks and tools such as MLflow, Kubeflow, Weights & Biases, or Terraform (preferred).
Experience with containerization using Docker or Kubernetes (preferred).
Familiarity with distributed data processing, data science libraries, or graph databases (preferred).