Senior Manager, Core Infrastructure Engineering

Facilitates the implementation of processes for machine learning (ML) model productionization to managers. Implements organizational standards around machine learning model readiness for deployment. Promotes organizational strategy around the automation of machine learning workflows. Promotes organizational strategy around trained model/system alignment with design criteria. Implements improvements to organizational processes for the identification and evaluation of potential data quality, security, and/or privacy issues and their impacts on modeling. Facilitates organizational troubleshooting and debugging support processes to address issues in machine learning infrastructure and workflow and create robust solutions. Alleviates the impact of obstacles to cross-functional collaboration efforts with multiple stakeholders to make, adopt and communicate technical decisions and shape the development and delivery of software. Implements organizational processes for the development, refinement, and maintenance of tools, platforms, environments, and services for internal use. Implements improvements to organizational processes for the development of efficient, bug-free code from scratch. Executes organizational strategy to maintain team awareness of current developments in the machine learning field and integration of this knowledge into model development.

Key

Machine

Learning and Data Modeling – Model Productionization:

–

Facilitates

the implementation of machine learning (ML) model productionization processes

and process improvements.

–

Uses

technical knowledge and business familiarity to empower the transformation of

machine learning prototypes into production-ready models.

–

Implements

strategy to build technical expertise and readiness across team related to

model productionization.

–

Alleviates

the impact of obstacles on 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.

Model

Development and Deployment – Model Deployment:

–

Implements

multiple team standards around ML model readiness for deployment (e.g., model

scaling, model code cleaning, and meeting production quality standards).

–

Promotes

multiple team strategy around 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.

Model

Development and Deployment – Model Performance:

–

Promotes

multiple team strategies around trained model/system alignment with design

criteria.

–

Identifies

improvements within multiple team processes around deployed model performance

evaluation and troubleshooting.

–

Facilitates

the creation of novel metrics that provide analytical insights to non-technical

stakeholders into how well machine learning models are operating.

Model

Development and Deployment – Data Quality:

–

Implements

improvements to multiple team processes for the identification and evaluation

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

security, and data privacy, and the minimization of their impacts on data

analyses and modeling.

–

Promotes

multiple team strategies for preparing for and enabling model training.

Internal

Collaborations and Impacts – Model Integration and Operation:

–

Implements

improvements to multiple team strategy that forms partnerships for

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

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

–

Maintains

accountability of model development and operations teams in the smooth

deployment and continuous improvement of ML models.

–

Builds

the team's knowledge of operational considerations of model deployment (e.g.,

performance, scalability, stability, maintenance) to facilitate multiple team

processes.

–

Facilitates

expert troubleshooting and debugging support efforts to address issues in

machine learning infrastructure and workflow and create robust solutions to

prevent future problems.

Internal

Collaborations and Impacts – Tool Development:

–

Implements

process improvements for the development, refinement, and maintenance of tools,

platforms, environments, and services for internal use.

Internal

Collaborations and Impacts – Coding and Documentation:

–

Implements

improvements to multiple team processes for the development of efficient,

bug-free code from scratch, as well as the maintenance and organization of the

existing codebase.

–

Maintains

team adherence to best practices for version control, code review, and code

delivery/deployment.

–

Monitors

professional documentation for technical processes (experimentation, data

collection and analyses, model building).

Machine

Learning Expertise:

–

Implements

multiple team strategies to maintain team awareness of current developments in

the machine learning field and integration of this knowledge into model

development.

–

Utilizes

familiarity with the usage and development of third-party machine learning

frameworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) to

identify improvements to multiple team processes around their performance and

scalability, and integrate them into production environments.

Core

Planning

& Execution:

–

Manages

multiple medium- to large-scale projects or initiatives across teams, ensuring

timelines, deliverables, and budgets when applicable are monitored and met.

–

Provides

direction to teams on project work, setting priorities, and aligning with

business needs.

–

Guides

teams on adjusting plans to accommodate resource or timeline changes.

Collaboration

& Partnership:

–

Drives

cross-functional partnerships to align expectations and shared objectives

across multiple teams.

–

Coaches

team members to develop strategic relationships with business leaders,

stakeholders, and external partners to foster collaboration and long-term

success.

–

Promotes

inclusivity by actively seeking and listening to diverse perspectives, ensuring

others feel heard and respected.

Problem

Solving:

–

Provides

direction to multiple teams on addressing complex operational and/or technical

issues as well as providing guidance on analyzing complex data and/or

information to identify solutions.

–

Reviews

and provides insights into unresolved or critical issues, helping the team to

identify potential solutions.

Continuous

Learning:

–

Models

engaging in continuous learning to deepen expertise and stay ahead of industry

trends, integrating best practices into strategic planning.

–

Leverages

feedback to drive personal and team skill improvements.

–

Identifies

skill gaps across teams, and empowers team members to pursue learning and

knowledge sharing opportunities that build their expertise in new areas and

coaches them to apply learnings to advance the organization.

Continuous

Improvement:

–

Drives

team to collaborate on, develop, and implement ideas to increase the efficiency

and effectiveness of processes, protocols, and workflows within and across

teams, providing oversight.

–

Guides

team to adopt new ideas for alternative approaches and methods and encourages

feedback for continued improvement.

Performance

and Development:

–

Drives

performance across teams by providing feedback and coaching in alignment with

performance management processes, guidelines, and expectations.

–

Discusses

development goals with team members, shares opportunities to facilitate career

development, and ensures individual goals are aligned with broader

organizational goals.

–

Develops

and manages talent acquisition pipeline by leading candidate interviews,

monitoring promotion eligibility, and/or orchestrating talent resources.

Career Level - M3