Senior Machine Learning Engineer
Amgen
Senior Machine Learning Engineer
Job Description
Career CategoryClinicalJob DescriptionWhat you will doLet'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 ResponsibilitiesDesign, build, and deploy production-grade ML services, APIs, and applications that integrate predictive models into LMD platforms and scientific workflowsPackage, containerize, and serve models for batch and real-time inferenceProductionize research models by improving reliability, scalability, testing, and maintainabilityEstablish MLOps practices for experiment tracking, model/version management, validation, deployment, and rollbackImplement CI/CD pipelines and software engineering best practices to ensure code quality, maintainability, security, and reproducibility across ML applicationsMonitor model performance, data quality, data/model drift, service health, usage and troubleshoot issuesBuild and maintain reproducible workflows for data preparation, model training, inference, and evaluation in collaboration with ML scientistsEvaluate and implement emerging MLOps, model observability, and ML platform technologies that improve deployment speed, reliability, and scalabilityCommunicate technical designs, trade-offs, metrics, and recommendations to technical and scientific partners What we expect of youWe 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 QualificationsDoctorate degree with 4+yrs in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or a related fieldOrMaster's degree and 8+ years of directly related experiencePreferred QualificationsExperience building and supporting production ML systems, model-serving platforms, APIs, or data-driven applicationsStrong Python programming and software engineering fundamentals, including testing, code review, documentation, packaging, and version controlHands-on experience with MLOps tools such as MLflow, model registries, experiment tracking, CI/CD and model lifecycle managementExperience with Docker, Kubernetes, REST/gRPC APIs, and cloud-native deployment patternsFamiliarity with AWS, Databricks, Spark, or similar cloud/data platformsExperience with model observability, logging, alerting, drift detection, and production troubleshootingFamiliarity with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or related libraries, and the ability to package models for reliable inferenceAbility to work effectively with scientists, ML researchers, data engineers, platform teams, and software engineersStrong 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..
Locations
- India - Hyderabad
Skills Required
- and supporting production ML systemsintermediate
- MLOps tools such as MLflowintermediate
- Dockerintermediate
- AWSintermediate
- model observabilityintermediate
- machine learning frameworks such as PyTorchintermediate
Required Qualifications
- Doctorate degree with 4+yrs in Data Science, Computer Science, Computational Biology, Bioinformatics, Computational Chemistry, or a related field (degree in data science)
- Master's degree and 8+ years of directly related experience (experience, 8 years)
Preferred Qualifications
- Experience building and supporting production ML systems, model-serving platforms, APIs, or data-driven applications (experience)
- Strong Python programming and software engineering fundamentals, including testing, code review, documentation, packaging, and version control (experience)
- Hands-on experience with MLOps tools such as MLflow, model registries, experiment tracking, CI/CD and model lifecycle management (experience)
- Experience with Docker, Kubernetes, REST/gRPC APIs, and cloud-native deployment patterns (experience)
- Familiarity with AWS, Databricks, Spark, or similar cloud/data platforms (experience)
- Experience with model observability, logging, alerting, drift detection, and production troubleshooting (experience)
- Familiarity with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or related libraries, and the ability to package models for reliable inference (experience)
- Ability to work effectively with scientists, ML researchers, data engineers, platform teams, and software engineers (experience)
- 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. (experience)
- Experience building and supporting production ML systems, model-serving platforms, APIs, or data-driven applications (experience)
- Strong Python programming and software engineering fundamentals, including testing, code review, documentation, packaging, and version control (experience)
- Hands-on experience with MLOps tools such as MLflow, model registries, experiment tracking, CI/CD and model lifecycle management (experience)
- Experience with Docker, Kubernetes, REST/gRPC APIs, and cloud-native deployment patterns (experience)
- Familiarity with AWS, Databricks, Spark, or similar cloud/data platforms (experience)
- Experience with model observability, logging, alerting, drift detection, and production troubleshooting (experience)
- Familiarity with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or related libraries, and the ability to package models for reliable inference (experience)
- Ability to work effectively with scientists, ML researchers, data engineers, platform teams, and software engineers (experience)
- 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. (experience)
Responsibilities
- 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.
- Design, build, and deploy production-grade ML services, APIs, and applications that integrate predictive models into LMD platforms and scientific workflows
- Package, containerize, and serve models for batch and real-time inference
- Productionize research models by improving reliability, scalability, testing, and maintainability
- Establish MLOps practices for experiment tracking, model/version management, validation, deployment, and rollback
- Implement CI/CD pipelines and software engineering best practices to ensure code quality, maintainability, security, and reproducibility across ML applications
- Monitor model performance, data quality, data/model drift, service health, usage and troubleshoot issues
- Build and maintain reproducible workflows for data preparation, model training, inference, and evaluation in collaboration with ML scientists
- Evaluate and implement emerging MLOps, model observability, and ML platform technologies that improve deployment speed, reliability, and scalability
- Communicate technical designs, trade-offs, metrics, and recommendations to technical and scientific partners
- Design, build, and deploy production-grade ML services, APIs, and applications that integrate predictive models into LMD platforms and scientific workflows
- Package, containerize, and serve models for batch and real-time inference
- Productionize research models by improving reliability, scalability, testing, and maintainability
- Establish MLOps practices for experiment tracking, model/version management, validation, deployment, and rollback
- Implement CI/CD pipelines and software engineering best practices to ensure code quality, maintainability, security, and reproducibility across ML applications
- Monitor model performance, data quality, data/model drift, service health, usage and troubleshoot issues
- Build and maintain reproducible workflows for data preparation, model training, inference, and evaluation in collaboration with ML scientists
- Evaluate and implement emerging MLOps, model observability, and ML platform technologies that improve deployment speed, reliability, and scalability
- Communicate technical designs, trade-offs, metrics, and recommendations to technical and scientific partners
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