Senior Machine Learning Engineer

Key
Responsibilities

Machine
Learning 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.

–        
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.

Model
Development and Deployment – Model Deployment:

–        
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.

Model
Development 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.

Model
Development 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.

Internal
Collaborations and Impacts – Model Integration and Operation:

–        
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.

Internal
Collaborations and Impacts – Tool Development:

–        
Contributes
to the development and maintenance of tools, platforms, environments, and
services for internal use.

Internal
Collaborations and Impacts – Coding and Documentation:

–        
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).

Machine
Learning Expertise:

–        
Develops
familiarity with current developments in the machine learning field and
integrates learnings into model development.

–        
Builds
familiarity with the usage and development of third-party machine learning
frameworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) to
continuously evaluate their performance and scalability, and integrate them
into production environments.

Core
Responsibilities

Planning
& Execution:

–        
Independently
manages work, monitoring timelines and deliverables to ensure projects or
initiatives stay on track and meet requirements.

–        
Proactively
prioritizes work and adapts to resource or timeline shifts, suggesting
adjustments to maintain project efficiency.

Collaboration
& Partnership:

–        
Collaborates
across teams to align on expectations and achieve shared objectives.

–        
Builds
and maintains a comprehensive understanding of business, stakeholder, and/or
customer needs to build and support effective partnerships.

–        
Actively
listens to diverse perspectives and asks questions to ensure understanding of
others.

Problem
Solving:

–        
Independently
identifies and addresses standard and non-standard issues in accordance with
standard practices, escalating more complex issues as appropriate.

–        
Analyzes
data and/or information from multiple sources to troubleshoot standard and
non-standard errors.

–        
Contributes
to knowledge sharing and best practices.

Continuous
Learning:

–        
Embraces
continuous learning by actively seeking to build knowledge and new skills
and/or tools and staying current with industry trends and best practices.

–        
Seeks
out and leverages feedback and training to improve skills.

–        
Contributes
to a culture of continuous learning and knowledge sharing with team members.

Continuous
Improvement:

–        
Develops
ideas and recommends updates to increase the efficiency and effectiveness of
processes, protocols, and workflows within a team.

–        
Seeks
input from team members on alternative approaches and methods for improving
work.