About this role
Lead the engineering of production machine learning systems at scale in an Agile environment. You will design ML architectures, build and review model/application code, implement CI/CD with test automation and monitoring, and ensure models are retrained, maintained, and monitored in production with Responsible and Explainable AI.
Key Responsibilities
- Design and deliver ML models and components for business problems
- Build and validate ML models while writing and testing application code
- Automate tests and deployment with CI/CD and monitoring
- Retrain, maintain, and monitor models in production
- Build cloud-based architectures and optimized data pipelines for ML
Technical Overview
This role focuses on productionizing ML applications, including ML architectural design, distributed-computing-based data-intensive solutions, and ML system scaling. Core stack centers on Python, Scala, or Java, with common ML frameworks such as scikit-learn, PyTorch, and Dask, plus CI/CD practices and high-availability performance requirements.
Ideal Candidate
The ideal candidate is a lead ML engineer with 6+ years designing and building data-intensive solutions on distributed computing and 2+ years scaling machine learning systems in production. They are strong in Python/Scala/Java, have built production data pipelines for ML, and can implement CI/CD with test automation and monitoring. They also prioritize Responsible and Explainable AI and risk-governed model operations.
Must-Have Skills
At least 6 years of experience designing and building data-intensive solutions using distributed computingAt least 4 years of experience programming with PythonScalaor JavaAt least 2 years of experience buildingscalingand optimizing ML systemsDesignbuildand/or deliver ML models and components that solve real-world business problemsSolve complex problems by writing and testing application codedeveloping and validating ML modelsand automating tests and deploymentRetrainmaintainand monitor models in productionEnsure all code is well-managed to reduce vulnerabilitiesModels are well-governed from a risk perspectiveML follows best practices in Responsible and Explainable AI
Nice-to-Have Skills
Master's or Doctoral Degree in computer scienceelectrical engineeringmathematicsor a similar field3+ years of experience building production-ready data pipelines that feed ML models3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learnPyTorchDask
Tools & Platforms
PythonScalaJavascikit-learnPyTorchDask
Required Skills
Machine learning engineeringproductionizing machine learning applicationsmachine learning architectural designmodel code developmentdistributed computinghigh availabilityperformanceretrainingmonitoring models in productioncontinuous integrationcontinuous deploymenttest automationmonitoringResponsible and Explainable AIPythonScalaJavascikit-learnPyTorchDaskhyperparameter tuningfeature selectionvalidationbias/variancedimensionality
Hard Skills
Machine learning engineeringProductionizing machine learning applications and systems at scaleMachine learning architectural designModel and application code developmentModel trainingHyperparameter tuningDimensionalityBias/varianceValidationFeature selectionChoosing modelAutomating tests and deploymentWriting and testing application codeHigh availabilityPerformance of machine learning applicationsRetraining models in productionMonitoring models in productionCloud-based architecturesCloud-based architectures for MLData pipelinesData pipeline optimizationContinuous integrationContinuous deploymentTest automationMonitoringVulnerability reductionResponsible and Explainable AIRisk governance for modelsProgramming languages: PythonScalaor JavaBig data and ML applicationsDistributed computingAgile team development
Soft Skills
Cross-functional collaborationCollaboration with Product and Data Science teamsCross-functional Agile team participationProblem-solvingContinuous learning
Keywords for Your Resume
Lead Machine Learning EngineerMachine Learning Engineer (MLE)MLEAgileDesignbuildand/or deliver ML modelsMachine learning architectural designproductionizing machine learning applicationsproduction-ready data pipelinesdistributed computinghigh availabilityperformanceretrainmonitor models in productioncontinuous integrationcontinuous deploymenttest automationmonitoringResponsible and Explainable AIPythonScalaJavascikit-learnPyTorchDaskhyperparameter tuningfeature selectionvalidationbias/variancedimensionality
Deal Breakers
Bachelor's Degree, 6+ years distributed computing for data-intensive solutions, 4+ years programming with Python, Scala, or Java, 2+ years building, scaling, and optimizing ML systems
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