Senior Machine Learning Engineer

Job Description

Implements machine learning (ML) models for production with minimal guidance. Contributes to the readiness of machine learning models for deployment in production. Contributes to the automation of machine learning workflows. Participates in the creation of infrastructure and frameworks to monitor the performance of machine learning models in deployment. Identifies potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Contributes to addressing issues in machine learning infrastructure and workflows. Collaborates with stakeholders to integrate machine learning models into new or extant systems. Contributes to the development and maintenance of tools, platforms, and services for internal use. Develops low-complexity, efficient, bug-free code from scratch. Develops familiarity with current developments in the machine learning field and integrates knowledge into model development.

Responsibilities

KeyResponsibilities

MachineLearning and Data Modeling – Model Productionization:

–

Utilizes

machine learning (ML) and software development knowledge to implement ML models

for production with minimal guidance.

–

Contributes

to transforming machine learning prototypes into production-ready models.

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Supports

collaboration with multiple stakeholders such as Development Leads, Product

Management, Operations, and Release Management to make, adopt, and communicate

technical decisions, and shape the development and delivery of software.

ModelDevelopment and Deployment – Model Deployment:

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Contributes

to ML model readiness for deployment by scaling models, cleaning model code,

and ensuring production quality standards are met.

–

Contributes

to the automation of machine learning workflows, from data extraction,

transformation, and loading (ETL) to model deployment and monitoring, to

establish the continuous integration and continuous delivery of machine

learning solutions.

ModelDevelopment and Deployment – Model Performance:

–

Utilizes

infrastructure and frameworks to monitor the performance and alignment with

design criteria of trained models and/or systems.

–

Monitors

the performance of deployed models and troubleshoots independently or in

collaboration with Data Science.

–

Interprets

novel metrics that provide analytical insights to non-technical stakeholders on

how well machine learning models are operating.

ModelDevelopment and Deployment – Data Quality:

–

Identifies

potential issues related to data quality (e.g., bias, fairness), data security,

and data privacy, and contributes to minimizing their impacts on data analyses

and modeling.

–

Contributes

to tasks such as data cleaning, preprocessing, and feature identification to

prepare for and enable model training.

InternalCollaborations and Impacts – Model Integration and Operation:

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Contributes

to collaboration with multiple stakeholders (e.g., data scientists, software

developers) to integrate ML models into new or existing systems.

–

Supports

the partnership between model development and operations, ensuring smooth

deployment and continuous improvement of ML models.

–

Learns

operational considerations of model deployment (e.g., performance, scalability,

stability, maintenance).

–

Participates

in troubleshooting and debugging support efforts, such as addressing issues in

machine learning infrastructure and workflow, and helping to create robust

solutions to prevent future problems.

InternalCollaborations and Impacts – Tool Development:

–

Contributes

to the development and maintenance of tools, platforms, environments, and

services for internal use.

InternalCollaborations and Impacts – Coding and Documentation:

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Contributes

to the development of efficient, bug-free, low-complexity code from scratch and

properly maintains and organizes the existing codebase.

–

Adheres

to best practices for version control, code review, and continuous integration

in machine learning projects.

–

Updates

and maintains professional documentation for technical processes

(experimentation, data collection and analyses, model building).

MachineLearning Expertise:

–

Develops

familiarity with cur