Senior Machine Learning Engineer

Amgen is a biotechnology company focused on developing medicines for serious illnesses. The Senior Machine Learning Engineer will help build scalable machine learning platforms and workflows, productionize AI/ML solutions for drug discovery and development, and support scientists in managing model lifecycles.

Responsibilities:

• Deliver AI and ML-enabled applications and platforms, with deployed models ranging from classical ML models to natural language processing, protein language models, and large language models
• Contribute to the development and maintenance of ML platform capabilities, including:
  • Data pipelines and feature engineering workflows
  • Model training, evaluation, and deployment pipelines
  • Experiment tracking and model registry systems
  • Model performance evaluations and monitoring
• Partner in implementation of AI and ML Ops best practices, including CI/CD, cloud infra as code, monitoring, traceability and reproducibility
• Collaborate with cross-functional teams to transition standards and outcomes from experimentation to production-grade enterprise solutions
• Build and maintain scalable and efficient, productionized MLOps solutions on cloud platforms
• Develop and deliver training content and knowledge articles to educate resident scientists on model lifecycle management best practices

Requirements:

• Doctorate degree
• Master's degree and 2 years of relevant experience in Computer Science, IT, or related field
• Bachelor's degree and 4 years of relevant experience in Computer Science, IT, or related field
• Associate's degree and 8 years of relevant experience in Computer Science, IT, or related field
• High school diploma / GED and 10 years of relevant experience in Computer Science, IT, or related field
• Software programming (Python, version control, test driven development)
• Experience with cloud technologies (AWS preferred, Databricks)
• Hands-on experience with AI/ML model training and serving
• Excellent critical-thinking and problem-solving skills
• Strong communication and collaboration skills
• Demonstrated presentation skills
• Self-motivated, independent ownership and leadership skills
• Experience with deployment and maintenance of AI/ML models in production
• Substantial experience with cloud technologies and platforms, such as Databricks, AWS (preferred), or Azure
• Experience with AI and MLOps frameworks and tools (MLflow, Kubeflow, Weights & Biases, Terraform, etc.)
• Experience with containerization (Docker, Kubernetes)
• Familiarity with distributed data processing (e.g., Spark)
• Familiarity with data science libraries (scikit-learn, TensorFlow, PyTorch, LangChain)
• Experience with graph databases