Machine Learning Engineer

Amgen-posted 4 months ago
Full-time • Entry Level
Hybrid • Thousand Oaks, CA
Chemical Manufacturing
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At Amgen, if you feel like you're part of something bigger, it's because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we've helped pioneer the world of biotech in our fight against the world's toughest diseases. With our focus on four therapeutic areas -Oncology, Inflammation, General Medicine, and Rare Disease- we reach millions of patients each year. As a member of the Amgen team, you'll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you'll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

  • 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 and improve efficiency.
  • Develop and implement monitoring systems to track model performance and identify issues.
  • Conduct A/B testing and experimentation to optimize model performance.
  • Work closely with data scientists, engineers, and product teams to deliver ML solutions.
  • Stay updated with the latest trends and advancements.
  • Solid foundation in machine learning algorithms and techniques.
  • Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow).
  • Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD).
  • Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Outstanding analytical and problem-solving skills.
  • Ability to learn quickly.
  • Good communication and interpersonal skills.
  • Experience with big data technologies (e.g., Spark, Hadoop), and performance tuning in query and data processing.
  • Experience with data engineering and pipeline development.
  • Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification.
  • Knowledge of NLP techniques for text analysis and sentiment analysis.
  • Experience in analyzing time-series data for forecasting and trend analysis.
  • A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions.
  • Group medical, dental and vision coverage.
  • Life and disability insurance.
  • Flexible spending accounts.
  • A discretionary annual bonus program.
  • Stock-based long-term incentives.
  • Award-winning time-off plans.
  • Flexible work models, including remote and hybrid work arrangements.
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