Machine Learning Engineer
Job Description
Amgen, a pioneering biotechnology company since 1980, is seeking a Machine Learning Engineer to join their innovative team. This role combines cutting-edge ML technology with life-changing healthcare solutions. The position offers an opportunity to work on significant projects that directly impact patient care through the development and deployment of machine learning solutions.
The role requires expertise in both machine learning and MLOps, focusing on building and scaling ML models from development to production. You'll be working with state-of-the-art tools and technologies, including cloud platforms, MLOps frameworks, and modern ML libraries. The position involves close collaboration with data scientists, engineers, and product teams to deliver sophisticated ML solutions that contribute to Amgen's mission of serving patients with serious illnesses.
As part of Amgen's engineering team, you'll contribute to four therapeutic areas: Oncology, Inflammation, General Medicine, and Rare Disease. The company's culture emphasizes collaboration, innovation, and science-based approaches, making it an ideal environment for professional growth in the intersection of technology and healthcare.
The role offers a unique opportunity to apply advanced ML engineering skills in a meaningful context, working on projects that have real-world impact on patient care. You'll be part of a company that has reached millions of patients worldwide and continues to develop breakthrough solutions in biotechnology. The position combines technical challenges with the satisfaction of contributing to significant healthcare advancements.
Responsibilities For Machine Learning Engineer
- Collaborate with data scientists to develop, train, and evaluate machine learning models
- Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring
- Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment
- Implement DevOps/MLOps best practices to automate ML workflows
- Develop and implement monitoring systems to track model performance
- Conduct A/B testing and experimentation to optimize model performance
- Work closely with data scientists, engineers, and product teams to deliver ML solutions
Requirements For Machine Learning Engineer
- Master's degree or Bachelor's degree with 5-9 years of experience
- Solid foundation in machine learning algorithms and techniques
- Experience in MLOps practices and tools (MLflow, Kubeflow, Airflow)
- Experience in DevOps tools (Docker, Kubernetes, CI/CD)
- Proficiency in Python and relevant ML libraries (TensorFlow, PyTorch, Scikit-learn)
- Outstanding analytical and problem-solving skills
- Good communication and interpersonal skills
Benefits For Machine Learning Engineer
- Competitive Benefits Package
