About this role
Build and productionize machine learning applications as a senior lead MLE on an Agile team. Own ML architectural design, code development and review, and end-to-end production operations including retraining, maintenance, monitoring, and CI/CD.
Key Responsibilities
- Design and deliver ML models and components for business problems
- Develop and review model and application code, ensuring high availability and performance
- Automate tests and deployments using continuous integration and continuous deployment
- Retrain, maintain, and monitor models in production
- Construct optimized data pipelines and collaborate on cross-functional Agile delivery
Technical Overview
Focuses on ML infrastructure and application code development, optimized data pipelines, and reliable deployments using continuous integration and continuous deployment. Applies Responsible and Explainable AI and risk governance while ensuring high availability and performance of ML systems.
Ideal Candidate
The ideal candidate is a senior/lead machine learning engineer (MLE) with 8+ years building data-intensive distributed computing solutions and 4+ years programming with Python, Scala, or Java. They have strong experience designing ML systems, creating optimized data pipelines, and productionizing models with continuous integration/continuous deployment and monitoring, while applying Responsible and Explainable AI and risk-governed development practices.
Must-Have Skills
Bachelor's Degree8 years of experience designing and building data-intensive solutions using distributed computing4 years of experience programming with PythonScalaor Java3 years of experience buildingscalingand optimizing ML systems2 years of experience leading teams developing ML solutionsdesignbuildand/or deliver ML models and componentsautomating tests and deploymentretrainmaintainand monitor models in productionconstruct optimized data pipelinescontinuous integration and continuous deployment best practicesResponsible and Explainable AIhigh availabilityperformance
Nice-to-Have Skills
Master's or doctoral degree in computer scienceelectrical engineeringmathematicsor a similar fieldExperience developing and deploying ML solutions in a public cloud such as AWSAzureor Google Cloud Platform4+ years of on-the-job experience wi
Required Skills
Machine Learning Engineer (MLE)Agiledistributed computingPythonScalaJavaML systemsmodel traininghyperparameter tuningfeature selectiondimensionalitybias/variancevalidationdata pipelinescontinuous integrationcontinuous deploymenttest automationmonitoringResponsible and Explainable AIhigh availabilityperformance
Hard Skills
machine learning architectural designmodel and application code developmenthigh availabilityperformance of machine learning applicationsmodel traininghyperparameter tuningfeature selectiondimensionalitybias/variancevalidationprogramming with Pythonprogramming with Scalaprogramming with Javadistributed computingdata-intensive solutionsML systems scalingoptimizing ML systemsautomating testsdeploymentretrainingmaintaining models in productionmonitoring models in productioncloud-based architecturescloud platformsdata pipelinescontinuous integrationcontinuous deploymenttest automationmonitoringResponsible and Explainable AIrisk governance of modelsvulnerability reduction
Soft Skills
cross-functional collaborationAgile teamworkproblem-solvingtechnical design communicationleadershipcontinuous learning
Keywords for Your Resume
Senior Lead Machine Learning EngineerMachine Learning EngineerMLEAgiledesignbuildand/or deliver ML modelsML infrastructuremodel traininghyperparameter tuningdimensionalitybias/variancevalidationapplication codeautomating tests and deploymentretrainmaintainmonitor models in productioncloud-based architecturesdata pipelinescontinuous integrationcontinuous deploymenttest automationmonitoringvulnerabilitiesResponsible and Explainable AIrisk perspectivehigh availabilityperformancePythonScalaJavadistributed computingAWSAzureGoogle Cloud Platform
Deal Breakers
Bachelor's Degree, 8 years designing and building data-intensive solutions using distributed computing, 4 years programming with Python, Scala, or Java, 3 years building, scaling, and optimizing ML systems, 2 years leading teams developing ML solutions
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