Associate Machine Learning Engineer

Amgen

About The Position

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. Associate Machine Learning Engineer What you will do Let's do this. Let's change the world. We are seeking a motivated Machine Learning Engineer with exposure to MLOps practices to join our team. In this role, you will support the development, deployment, and maintenance of machine learning solutions under the guidance of senior engineers and data scientists. You will contribute to building reliable ML pipelines and help operationalize models in production environments.

Requirements

Bachelor's degree OR Associate's degree and 4 years of computer science, IT, or a related field OR High school diploma / GED and 6 years of computer science, IT, or a related field
Solid understanding of machine learning fundamentals (regression, classification, clustering).
Hands-on experience or exposure to MLOps tools and practices (e.g., MLflow, Airflow, Kubeflow) and DevOps tools (Docker, basic Kubernetes, CI/CD).
Proficiency in Python and common ML libraries (Scikit-learn, TensorFlow, or PyTorch).
Strong analytical and problem-solving skills.
Good communication skills and ability to collaborate effectively in a team environment.
Ability to learn quickly and adapt to new tools and technologies.
Good analytical and troubleshooting abilities.
Clear verbal and written communication skills.
Ability to work effectively with global and virtual teams.
Demonstrates initiative and willingness to learn.
Able to manage assigned tasks and priorities with guidance.
Team-oriented mindset with a focus on shared goals.
Organized and detail oriented.

Nice To Haves

Exposure to big data technologies such as Spark or Hadoop.
Experience supporting data pipelines or working with data engineering teams.
Basic understanding of statistical concepts and hypothesis testing.
Familiarity with NLP or time-series analysis concepts.

Responsibilities

Collaborate with data scientists and senior ML engineers to develop, train, and evaluate machine learning models.
Assist in building and maintaining ML pipelines for data ingestion, feature engineering, model training, deployment, and monitoring.
Support the use of cloud platforms (AWS, Azure, or GCP) for ML model development and deployment.
Implement basic MLOps and DevOps practices to automate and streamline ML workflows.
Help monitor deployed models for performance, data drift, and operational issues, escalating concerns as needed.
Participate in experimentation and A/B testing efforts to improve model performance.
Work closely with data scientists, engineers, and product teams to deliver ML solutions aligned with business needs.
Stay current with emerging machine learning and MLOps tools and best practices.

Benefits

health and welfare plans for staff and eligible dependents
financial plans with opportunities to save towards retirement or other goals
work/life balance
career development opportunities
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, or for field sales representatives, a sales-based incentive plan
Stock-based long-term incentives
Award-winning time-off plans
Flexible work models where possible