SeniorMachine Learning Engineer

In this role, you will contribute to the development of scalable machine learning platforms and workflows that enable scientists and researchers across Amgen to build, deploy, and manage AI/ML models. You will work closely with experienced engineers, data scientists, and domain experts to productionize machine learning solutions primarily in support of drug discovery and development.

This is an ideal role for engineers early in their ML engineering careers who want to grow their expertise in MLOps, cloud platforms, and applied AI in life sciences.

Roles Responsibilities:

Deliver AI and ML-enabled applications, 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, 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

Required Skills:

Software programming (python, version control, test driven development)
Familiarity with cloud technologies (AWS preferred, Databricks)
Hands-on experience with AI/ML model training and serving

Preferred qualifications:

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)

Soft Skills:

Excellent critical-thinking and problem-solving skills
Strong communication and collaboration skills
Demonstrated presentation skills
Self-motivated, independent ownership and leadership skills