Senior Machine Learning Engineer
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At a glance
Requirements
Credentials this posting asks for.
Job overview
Amgen is hiring a Senior Machine Learning Engineer. Amgen's AI & Data for Engineered Biologics team is seeking a Software/ML Engineer to integrate predictive models and ML-enabled tools into production for biologics discovery. This role involves partnering with ML scientists, software engineers, data engineers, and discovery teams to transform research prototypes into scalable, tested, and maintainable services. The engineer will build MLOps foundations to facilitate easier deployment, reproduction, monitoring, and integration of models into scientific workflows.
Key focus areas include Design, build, and deploy production-grade ML services, APIs, and applications, Package, containerize, and serve models for batch and real-time inference, and Productionize research models by improving reliability, scalability, testing, and maintainability.
Successful candidates bring Doctorate Degree In Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, Or Related Field, 4+ Years Directly Related Experience With Doctorate, and Master's Degree. Important skills include Design ML Services, Build ML Services, Deploy ML Services, Containerize Models, Serve Models, and Improve Reliability. Preferred (not required): Package Models, Building Production ML Systems, Supporting Production ML Systems, and Model-serving Platforms.
Skills & qualifications
Skills
Qualifications
Full job description
What you will do
Let's do this. Let's change the world. Amgen’s AI & Data for Engineered Biologics team within Large Molecule Discovery is seeking a Software/ML Engineer to help bring predictive models and ML-enabled tools into production for biologics discovery.
In this role, you will partner with ML scientists, software engineers, data engineers, and discovery teams to transform research prototypes into scalable, tested, and maintainable services. You will build the MLOps foundations that make models easier to deploy, reproduce, monitor, and integrate into scientific workflows.
Key Responsibilities
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Design, build, and deploy production-grade ML services, APIs, and applications that integrate predictive models into LMD platforms and scientific workflows
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Package, containerize, and serve models for batch and real-time inference
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Productionize research models by improving reliability, scalability, testing, and maintainability
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Establish MLOps practices for experiment tracking, model/version management, validation, deployment, and rollback
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Implement CI/CD pipelines and software engineering best practices to ensure code quality, maintainability, security, and reproducibility across ML applications
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Monitor model performance, data quality, data/model drift, service health, usage and troubleshoot issues
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Build and maintain reproducible workflows for data preparation, model training, inference, and evaluation in collaboration with ML scientists
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Evaluate and implement emerging MLOps, model observability, and ML platform technologies that improve deployment speed, reliability, and scalability
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Communicate technical designs, trade-offs, metrics, and recommendations to technical and scientific partners
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The collaborative professional we seek is a Software/ML Engineer with these qualifications.
Basic Qualifications
Doctorate degree with 4+yrs in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or a related field
Or
Master's degree and 8+ years of directly related experience
Preferred Qualifications
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Experience building and supporting production ML systems, model-serving platforms, APIs, or data-driven applications
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Strong Python programming and software engineering fundamentals, including testing, code review, documentation, packaging, and version control
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Hands-on experience with MLOps tools such as MLflow, model registries, experiment tracking, CI/CD and model lifecycle management
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Experience with Docker, Kubernetes, REST/gRPC APIs, and cloud-native deployment patterns
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Familiarity with AWS, Databricks, Spark, or similar cloud/data platforms
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Experience with model observability, logging, alerting, drift detection, and production troubleshooting
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Familiarity with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or related libraries, and the ability to package models for reliable inference
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Ability to work effectively with scientists, ML researchers, data engineers, platform teams, and software engineers
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Strong ownership, problem-solving, and communication skills, with demonstrated contributions to production ML systems, open-source MLOps tools, or publications in venues such as MLSys, NeurIPS, ICML, ICLR, or comparable venues; candidates should highlight representative work on their resume.
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